Multiple-classifier endometrial cancers: the first multicenter clinicopathological study from five Polish institutions

Multiple-classifier endometrial cancers: the first multicenter clinicopathological study from five Polish institutions | 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 Multiple-classifier endometrial cancers: the first multicenter clinicopathological study from five Polish institutions Wiktor Szatkowski, Małgorzata Nowak-Jastrząb, Marcin Misiek, Tomasz Kluz, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6538787/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Endometrial cancer (EC) exhibits molecular heterogeneity, classified into four subtypes: POLEmut (POLE ultramutated), MMRd (mismatch repair deficient), p53abn (p53 abnormal), and NSMP (no specific molecular profile). Tumors with multiple classifiers (multiple-classifier ECs) challenge risk stratification, particularly MMRd-p53abn and POLEmut-related subtypes. Method: This study aims to assess the prevalence and clinicopathological features of multiple-classifier ECs, focusing on MMRd-p53abn versus classical MMRd and p53abn, and their ESGO/ESTRO/ESP risk group alignment. Results: We analyzed 1205 EC patients from five Polish oncology centers, molecularly profiled post-surgery. Subtypes included MMRd, p53abn, POLEmut, and multiple-classifier groups (MMRd-p53abn, POLEmut-p53abn, POLEmut-MMRd, POLEmut-MMRd-p53abn). Histotype, grade, myometrial invasion, lymphovascular space invasion, and risk groups classification were assessed. Multiple-classifier ECs comprised 7.5% (90/1205), with MMRd-p53abn at 4.2% (51/1205), POLEmut-p53abn 0.3% (4/1205), POLEmut-MMRd 1.9% (23/1205), and POLEmut-MMRd-p53abn 1.0% (12/1205). MMRd-p53abn tumors showed more non-endometrioid histology (9.8% vs 2.86%, p=0.035), high-grade (G3) tumors (27.45% vs 12.14%, p=0.005), and assignment to high-intermediate or high-risk (HIR/HR) groups (62.75% vs 37.50%, p=0.001) than MMRd, but less non-endometrioid histology than p53abn (9.8% vs 35.63%, p=0.001). POLEmut-p53abn tumors had higher G3 (75.00% vs 5.88%, p=0.005) and more FIGO III-IV stages (75.00% vs 5.88%, p=0.005) than POLEmut. Conclusions: MMRd-p53abn tumors are more aggressive than MMRd yet less adverse than p53abn, while POLEmut-p53abn diverge from POLEmut’s favorable prognosis. Their frequent assignment to high-intermediate or high-risk (HIR/HR) groups suggests refined risk stratification is needed. As Poland’s first multi-center EC study, these data may inform future guidelines. Endometrial cancer multiple-classifier tumors MMRd p53abn POLEmut molecular classification risk stratification clinicopathological characteristics Figures Figure 1 Figure 2 Figure 3 Introduction Endometrial cancer (EC) is a major gynecological malignancy with a rising global burden, with 417,367 new cases and 97,370 deaths reported in 2020 [ 1 ]. In the United States, EC incidence increased by 40% from 2012 to 2022, driven by aging populations and rising prevalence of risk factors such as obesity and diabetes [ 2 , 3 ]. While early-stage EC has favorable outcomes, metastatic disease carries a five-year survival rate of only 17% [ 4 ], highlighting the need for improved risk stratification and personalized therapies. The Cancer Genome Atlas (TCGA) established four molecular subtypes of EC: POLEmut (ultramutated, best prognosis), MMRd (mismatch repair deficient, intermediate risk), p53abn (copy-number high, worst prognosis), and NSMP (copy-number low, good-to-intermediate prognosis) [ 5 ]. Validated by the ProMisE system, these subtypes guide treatment decisions in ESGO/ESTRO/ESP 2021, ESMO, and FIGO 2023 guidelines [ 6 , 7 ]. However, multiple-classifier ECs—tumors exhibiting features of more than one subtype, such as MMRd-p53abn—pose challenges due to their uncertain prognostic and therapeutic implications [ 6 , 8 ]. The MMRd-p53abn subgroup, in particular, raises questions about its intermediate (MMRd) versus high-risk (p53abn) status, with limited data on its clinicopathological characteristics and risk group assignment [ 9 ]. Moreover, regional differences in EC molecular profiles remain underexplored, especially in Central Europe. This study, the first multi-center analysis of EC in Poland, aims to evaluate the prevalence and clinicopathological features of multiple-classifier ECs, with a focus on MMRd-p53abn, within the ESGO/ESTRO/ESP risk stratification framework. By addressing gaps in the literature regarding these complex subtypes and providing unique data from an underrepresented population, our findings contribute to refining risk stratification and informing future therapeutic strategies for EC management. Materials and Methods 2.1. Study Design and Population This multi-center study enrolled 1205 consecutive endometrial cancer (EC) patients treated between April 2022 and December 2024 across five oncology centers in southeastern Poland (Silesia, Lesser Poland, Subcarpathia, Holy Cross, and Lublin regions). Molecular profiling was performed as part of routine clinical practice following surgical treatment, with clinicopathological features (histotype, grade, myometrial invasion, lymphovascular space invasion (LVSI) assessed per ESGO/ESTRO/ESP 2021 guidelines. Patients were categorized into molecular subtypes (MMRd, p53abn, POLEmut, NSMP) based on immunohistochemistry (IHC) and sequencing results, using a surrogate classification approach consistent with the ProMisE system. Multiple-classifier ECs were identified when tumors exhibited features of more than one molecular subtype. 2.2. Immunohistochemistry (IHC) Analysis IHC analysis of mismatch repair (MMR) proteins (MLH1, MSH2, MSH6, PMS2) and p53 was conducted uniformly across centers using OptiView and UltraView kits (Ventana, Roche Diagnostics, Indianapolis, IN, USA) on the BenchMark Ultra system, as described previously [ 8 ]. MMR deficiency (MMRd) was defined as loss of expression of at least one MMR protein; p53 abnormality (p53abn) was classified as a mutant or null staining pattern. 2.3. DNA Extraction and Molecular Analysis DNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissues using QIAamp DSP DNA FFPE Tissue Kit (Qiagen, Hilden, Germany) or Maxwell® RSC DNA FFPE Kit (Promega, Madison, WI, USA), following methods adapted from Szatkowski et al. [ 10 ]. POLE mutations were assessed by Sanger sequencing of exons 9, 11, 13, and 14, with pathogenicity classified per León-Castillo et al. [ 8 ]. Next-generation sequencing (NGS) on the IonTorrent platform, using a custom panel (Thermo Fisher Scientific Ion AmpliSeq Designer), targeted MLH1, MSH2, MSH6, PMS2, POLE, and TP53. 2.4. Variant Classification Genetic variants were classified using ClinVar, OncoKB, and Varsome databases as pathogenic, likely pathogenic, variants of unknown significance (VUS), likely benign, or benign. Only pathogenic and likely pathogenic variants were considered relevant due to their established clinical significance. 2.5. Standardization To ensure consistency across centers, IHC for MMR and p53, and NGS for POLE and TP53, followed identical protocols and panels, with uniform clinicopathological criteria, building on Szatkowski et al. [ 10 ]. 2.6. Statistical Analysis Statistical analyses were performed using SPSS version 27 (IBM Corp., Armonk, NY, USA). Group comparisons used chi-square or Fisher’s exact tests for categorical variables and Student’s t-test or Mann-Whitney U test for continuous variables. Statistical significance was set at p < 0.05. 2.7. Ethical Considerations The study adhered to the Declaration of Helsinki and was approved by the Institutional Review Board of the Maria Skłodowska-Curie National Research Institute of Oncology, Krakow Branch, Poland (Approval No. 6/2025, issued on 9 January 2025). Although this study was retrospective and involved anonymized data, additional ethical approval was sought and granted to ensure compliance with the highest standards of ethical research practices. Written informed consent was obtained from all subjects at the start of treatment. The consent included explicit permission to use their medical data for retrospective analysis and research purposes. All patient data were anonymized prior to analysis, in strict adherence to GDPR regulations, ensuring the confidentiality and privacy of all participants. Results 3.1. Prevalence of Multiple-Classifier Tumors Among 1205 patients, multiple-classifier endometrial cancers (ECs) accounted for 7.5% (90/1205), including MMRd-p53abn (4.2%, 51/1205), POLEmut-p53abn (0.3%, 4/1205), POLEmut-MMRd (1.9%, 23/1205), and POLEmut-MMRd-p53abn (1.0%, 12/1205). This prevalence is lower than the 11.4% reported by De Vitis et al.[9], which may be attributed to differences in diagnostic criteria or population characteristics. 3.2. MMRd, p53abn, and MMRd-p53abn Comparison Table 1 presents the clinicopathological characteristics of MMRd, p53abn, and MMRd-p53abn tumors. Compared to classical MMRd (N=280), MMRd-p53abn tumors (N=51) demonstrated: • Higher rates of non-endometrioid histology (9.8% vs 2.86%, p=0.035). • More frequent high-grade (G3) tumors (27.45% vs 12.14%, p=0.005). • Greater proportion assigned to high-intermediate or high-risk (HIR/HR) groups (62.75% vs 37.50%,p=0.001). In contrast, compared to p53abn tumors (N=174), MMRd-p53abn tumors exhibited a significantly lower proportion of non-endometrioid histology (9.8% vs 35.63%, p=0.001). These comparisons are visually represented in Figure 1 (Histotype comparison), Figure 2 (Grade comparison), and Figure 3 (Risk group comparison). Compared to MMRd, MMRd-p53abn demonstrated higher odds of non-endometrioid histology (OR=3.70, 95% CI: 1.15–11.89, p=0.035), high-grade (G3) tumors (OR=2.96, 95% CI: 1.45–6.05, p=0.005), and classification into high-intermediate or high-risk (HIR/HR) groups (OR=2.81, 95% CI: 1.50–5.25, p=0.001). 3.3. POLEmut and Multiple-Classifier POLEmut Comparison Table 2 compares classical POLEmut (N=34) with multiple-classifier POLEmut tumors. POLEmut-p53abn (N=4) exhibited more G3 tumors (75.00% vs 5.88%, p=0.005) and advanced FIGO stages III-IV (75.00% vs 5.88%, p=0.005) than POLEmut, despite limited cases. 3.4. Comparison with De Vitis et al.[9] Table 3 summarizes the prevalence of multiple-classifier ECs from our cohort and the De Vitis et al.[9] study. The proportion of MMRd-p53abn was similar between the two studies (4.2% vs 6.6%). However, POLEmut-p53abn was less frequent in our cohort (0.3% vs 3.6%), while POLEmut-MMRd-p53abn was more common (1.0% vs 0.7%) [9]. This comparison suggests potential differences in classification criteria or patient population. Table 1. Clinicopathological Characteristics of Patients with Endometrial Cancer by Molecular Subtype Variable Group MMRd (A) (N=280) p53abn (B) (N=174) MMRd-p53abn (C) (N=51) p-value (A vs C) OR (95% CI, A vs C) p-value (B vs C) MMRd + MMRd-p53abn (N=331) Age at surgery (years) Mean (SD) 66.05 (9.62) 68.19 (9.90) 67.81 (11.85) 0.291 0.718 66.32 (9.99) Age at surgery 70 years 103 (36.79%) 83 (47.70%) 25 (49.02%) 128 (38.67%) Unknown 1 (0.36%) 2 (1.15%) 1 (1.96%) 2 (0.60%) Histology Endometrioid 272 (97.14%) 112 (64.37%) 46 (90.20%) 0.035* 3.70 (1.15–11.89) 0.001* 318 (96.07%) Non-endometrioid 8 (2.86%) 62 (35.63%) 5 (9.80%) 13 (3.93%) Grade G1–G2 244 (87.14%) 95 (54.60%) 34 (66.67%) 0.005* 2.96 (1.45–6.05) 0.438 278 (83.99%) G3 34 (12.14%) 55 (31.61%) 14 (27.45%) 48 (14.50%) Unknown 2 (0.71%) 24 (13.79%) 3 (5.88%) 5 (1.51%) LVSI Absent or focal 211 (75.36%) 110 (63.22%) 30 (58.82%) 0.054 0.818 241 (72.81%) Diffuse 68 (24.29%) 61 (35.06%) 19 (37.25%) 87 (26.28%) Unknown 1 (0.36%) 3 (1.72%) 2 (3.92%) 3 (0.91%) Myometrial invasion < 1/2 156 (55.71%) 90 (51.72%) 21 (41.18%) 0.091 0.277 177 (53.47%) ≥ 1/2 122 (43.57%) 83 (47.70%) 29 (56.86%) 151 (45.62%) Unknown 2 (0.71%) 1 (0.57%) 1 (1.96%) 3 (0.91%) Cervical involvement No 205 (73.21%) 116 (66.67%) 33 (64.71%) 0.359 1.000 238 (71.90%) Yes 74 (26.43%) 58 (33.33%) 17 (33.33%) 91 (27.49%) Unknown 1 (0.36%) 0 (0.00%) 1 (1.96%) 2 (0.60%) Lymph node metastases No 237 (84.64%) 138 (79.31%) 38 (74.51%) 0.067 0.721 275 (83.08%) Yes 17 (6.07%) 19 (10.92%) 7 (13.73%) 24 (7.25%) Unknown 26 (9.29%) 17 (9.77%) 6 (11.76%) 32 (9.67%) Distant metastases No 224 (80.00%) 124 (71.26%) 37 (72.55%) 1.000 0.208 261 (78.85%) Yes 3 (1.07%) 9 (5.17%) 0 (0.00%) 3 (0.91%) Unknown 53 (18.93%) 41 (23.56%) 14 (27.45%) 67 (20.24%) FIGO stage Early (I–II) 237 (84.64%) 127 (72.99%) 38 (74.51%) 0.192 0.854 275 (83.08%) Advanced (III–IV) 43 (15.36%) 46 (26.44%) 12 (23.53%) 55 (16.62%) Unknown 0 (0.00%) 1 (0.57%) 1 (1.96%) 1 (0.30%) Risk group** Low or Intermediate 175 (62.50%) 49 (28.16%) 19 (37.25%) 0.001* 2.81 (1.50–5.25) 0.319 194 (58.61%) High-Intermediate and Higher 105 (37.50%) 122 (70.11%) 32 (62.75%) 137 (41.39%) Notes : Abbreviations: MMRd = mismatch repair deficient; p53abn = p53 abnormal; LVSI = lymphovascular space invasion; FIGO = International Federation of Gynecology and Obstetrics; SD = standard deviation; OR = odds ratio; CI = confidence interval. Statistical significance: *p < 0.05. Columns: MMRd (A): Classical MMRd tumors; p53abn (B): Classical p53abn tumors; MMRd-p53abn (C): Multiple-classifier MMRd-p53abn tumors; MMRd + MMRd-p53abn: Combined MMRd and MMRd-p53abn cases. p-values: Calculated for comparisons between MMRd vs MMRd-p53abn and p53abn vs MMRd-p53abn. OR (95% CI): Calculated for comparisons with p<0.05 (MMRd vs MMRd-p53abn) using chi-square or Fisher’s exact test. **ESGO/ESTRO/ESP 2020 guidelines applied; molecular classification per ProMisE. Table 2. Comparison of POLEmut and Multiple-Classifier POLEmut Tumors Variable POLEmut (N=34) POLEmut-MMRd (N=23) POLEmut-p53abn (N=4) POLEmut-MMRd-p53abn (N=12) p-value (POLEmut vs POLEmut-MMRd) p-value (POLEmut vs POLEmut-p53abn) p-value (POLEmut vs POLEmut-MMRd-p53abn) Grade G3 2 (5.88%) 4 (17.39%) 3 (75.00%) 2 (16.67%) 0.198 0.005* 0.247 Lymph node metastases 1 (2.94%) 1 (4.35%) 1 (25.00%) 2 (16.67%) 1.000 0.123 0.192 FIGO stage III–IV 2 (5.88%) 2 (8.70%) 3 (75.00%) 4 (33.33%) 1.000 0.005* 0.033* Notes: Abbreviations: POLEmut = POLE ultramutated; MMRd = mismatch repair deficient; p53abn = p53 abnormal; FIGO = International Federation of Gynecology and Obstetrics. Statistical significance: *p < 0.05. Data presentation: Values are presented as n (%) unless otherwise specified. p-values: Calculated for comparisons between POLEmut and each multiple-classifier subgroup. Table 3. Comparison of Multiple-Classifier Endometrial Cancers: Current Study vs De Vitis et al.[10] Tumor Type Current Study (N=1205) De Vitis et al. (2024) (N=422) MMRd-p53abn 4.2% (51/1205) 6.6% (28/422) POLEmut-p53abn 0.3% (4/1205) 3.6% (15/422) POLEmut-MMRd 1.9% (23/1205) 0.5% (2/422) POLEmut-MMRd-p53abn 1.0% (12/1205) 0.7% (3/422) Notes: Abbreviations: MMRd = mismatch repair deficient; POLEmut = POLE ultramutated; p53abn = p53 abnormal. Data presentation: Values are presented as percentage (%) followed by the number of cases (n/N). Source: De Vitis et al.[9] data are cited for comparative purposes Discussion 4.1. Significance of Results in the Context of Literature The present study aimed to evaluate the clinical and pathological characteristics of multiple-classifier endometrial cancers, particularly focusing on the MMRd-p53abn subtype, to better understand their prognostic implications. The advent of molecular classification has transformed our understanding of endometrial cancer (EC) biology [11,12,13]. In our study, multiple-classifier ECs comprised 7.5% of cases (90/1205), consistent with the reported range of 3–11% [14,15,9]. This detection rate may reflect the application of advanced diagnostics, such as next-generation sequencing (NGS) and detailed immunohistochemical (IHC) analysis of MMR and p53 proteins [16,17]. Multiple-classifier ECs with POLEmut are typically associated with favorable prognosis, even when co-occurring with MMRd/MSI-H and/or p53abn, as the ultramutated POLE profile appears to override other classifiers’ prognostic impact [5;8]. However, our analysis focuses on MMRd-p53abn, the most common multiple-classifier subtype in our cohort (4.2%, 51/1205), highlighting the importance of better characterizing its clinical behavior and risk classification. Particular focus is placed on the MMRd-p53abn subgroup, whose classification remains debated. In the ProMisE system, MMRd tumors are deemed intermediate-risk [6]. However, our results demonstrate a more aggressive phenotype for MMRd-p53abn compared to classical MMRd, with increased non-endometrioid histology (9.8% vs 2.86%, p=0.035), higher prevalence of high-grade tumors (27.45% vs 12.14%, p=0.005), and greater assignment to risk groups above high-intermediate risk (HIR) (62.75% vs 37.50%, p=0.001). Still, they are less adverse than p53abn, as evidenced by a lower rate of non-endometrioid histology (9.8% vs 35.63%,p=0.001). Comparison with León-Castillo et al. [8] highlights discrepancies between our findings. While their analysis of 1031 cases suggested that MMRd-p53abn should be treated similarly to MMRd in terms of prognosis, our results indicate a distinct subgroup with more aggressive features. This difference may be due to a lack of survival data in our cohort, but it aligns with reports from Kato et al.[18], Michalova et al.[19], and De Vitis et al.[9], which document a more adverse clinical profile for MMRd-p53abn. In particular, De Vitis et al.[9] reported a prevalence of 6.6% for MMRd-p53abn within a cohort of 422 patients, higher than our findings (4.2%). The variation may be due to differences in diagnostic criteria, population characteristics, or methodological factors. Nonetheless, the clinicopathological profile described by De Vitis et al.[9] is largely consistent with our findings, including associations with high-grade tumors and more advanced stages. The observed differences in MMRd-p53abn prevalence and characteristics compared to De Vitis et al.[9] and León-Castillo et al.[8] may partly stem from variations in molecular profiling techniques. Our study utilized a surrogate classification combining immunohistochemistry (IHC) for MMR and p53 with targeted sequencing (Sanger sequencing for POLE mutations and limited NGS panels), whereas other studies may have employed broader NGS panels or different IHC thresholds for defining classifier overlap. These methodological variations underscore the need for standardized molecular profiling protocols to enhance comparability across studies. 4.2. Clinical Implications The ESGO/ESTRO/ESP 2021 guidelines and ProMisE classification categorize MMRd-p53abn as MMRd, potentially underestimating their aggressive potential. Our data show MMRd-p53abn are more frequently assigned to HIR and high-risk (HR) groups (62.75%) than classical MMRd (37.50%), suggesting these patients may require more intensive adjuvant therapy than standard MMRd protocols. This is supported by Kato et al.[18] and Michalova et al.[19], who note features closer to p53abn, prompting consideration of HR reclassification. De Vitis et al.[9] also advocate for tailored therapeutic approaches given their aggressive clinicopathological profile. Unlike León-Castillo et al.[8], our study is based on detailed clinicopathological analysis rather than long-term survival data. However, our findings are consistent with the observations of Bogani et al.[20], who reported that MMRd-p53abn patients experienced increased risk of recurrence despite adjuvant treatment. If further studies confirm these findings, an independent risk category for MMRd-p53abn could be justified, potentially influencing treatment strategies such as radiotherapy or targeted therapies. 4.3. Need for Further Research Our study reveals distinct clinicopathological differences between MMRd and MMRd-p53abn, yet the absence of OS and PFS data limits prognostic conclusions. Further studies should determine if MMRd-p53abn warrant HR classification and more aggressive therapies than classical MMRd. The divergence from León-Castillo et al. [8] underscores the need for validation through prospective trials. 4.4. Novel Therapeutic Approaches and Study Implications Immunotherapy, such as dostarlimab in GARNET and RUBY trials [21], and combination regimens (PD-1 inhibitors + PARP inhibitors) may offer potential benefits for selected MMRd-p53abn patients (Mirza et al.[21]). However, their efficacy in these specific multiple-classifier subtypes awaits confirmation through clinical trials with long-term OS and PFS assessment. These therapeutic advances could be particularly relevant for MMRd-p53abn patients, given their aggressive clinicopathological profile identified in our cohort. As the first multi-center study in Poland, encompassing 1205 patients across five institutions, our analysis offers a unique contribution to the literature. The MMRd-p53abn frequency (4.2%) differs from other populations (e.g., 6.6% in De Vitis et al.) [9], potentially reflecting cohort-specific characteristics, which merits further exploration. These findings could inform future clinical guidelines and personalized treatment strategies for endometrial cancer patients with complex molecular profiles. 4.5. Limitations. This study has several limitations. The absence of overall survival (OS) and progression-free survival (PFS) data limits the ability to draw conclusions about the prognostic implications of multiple-classifier endometrial cancers, with findings relying on clinicopathological surrogates such as histology, grade, and FIGO stage. Small subgroup sizes, particularly for POLEmut-p53abn (N=4) and POLEmut-MMRd-p53abn (N=12), restrict the reliability of statistical inferences, necessitating cautious interpretation and validation in larger cohorts. The lack of multivariate analysis to adjust for potential confounders, such as age, FIGO stage, or lymphovascular space invasion, limits the assessment of the independent impact of molecular subtypes. Additionally, detailed data on lost mismatch repair (MMR) protein pairs (e.g., MLH1/PMS2, MSH2/MSH6) were not available, restricting the characterization of MMRd subtypes. The heterogeneity of secondary p53 alterations (e.g., clonal vs subclonal) was not assessed due to limited molecular data, which may affect the understanding of multiple-classifier tumor behavior. Detailed concordance between IHC, Sanger sequencing, and next-generation sequencing (NGS) results was not evaluated, as it was beyond the scope of this study. Further prospective studies with comprehensive molecular and clinical data are needed to validate these findings and assess their therapeutic significance. Abbreviations EC- Endometrial cancer POLEmut - POLE ultramutated MMRd -mismatch repair deficient p53abn -p53 abnormal NSMP-no specific molecular profile HIR/HR- high-risk G3- high-grade TCGA- The Cancer Genome Atlas LVSI- lymphovascular space invasion IHC- immunohistochemistry NGS- Next-generation sequencing VUS- variants of unknown significance OS-overall survival PFS- progression free survival Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Maria Skłodowska-Curie National Research Institute of Oncology (NIO-PIB), Kraków Branch, Poland (Approval No. 6/2025, issued on 9 January 2025). Although this study was retrospective and involved anonymized data, additional ethical approval was sought and granted to ensure compliance with the highest standards of ethical research practices. Informed Consent Statement At the start of the treatment, written informed consent was obtained from all subjects involved in this study. The consent included explicit permission to use their medical data for retrospective analysis and research purposes. All patient data were anonymized prior to analysis, in strict adherence to GDPR regulations, ensuring the confidentiality and privacy of all participants. Consent for publication Not applicable Data Availability Statement The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s). Conflicts of Interest The authors declare the following potential conflicts of interest: Wiktor Szatkowski reports having received lecture fees from AstraZeneca; Małgorzata Nowak-Jastrząb reports having received lecture fees and travel grants from AstraZeneca, MSD; Marcin Misiek reports no conflict of interest; Tomasz Kluz reports having received lecture fees and travel grants from AstraZeneca, GSK; Aleksandra Kmieć reports no conflict of interest; Małgorzata Cieślak-Steć reports having received lecture fees from AstraZeneca, GSK and participation in clinical trials for AstraZeneca, MSD, PSI, Medpace, Roche, Syneos Health; Magdalena Śliwińska reports no conflict of interest; Izabela Winkler reports no conflict of interest; Jacek Tomaszewski reports having received lecture fees from GSK, Gedeon Richter; Jerzy Jakubowicz reports no conflict of interest; Renata Pacholczak-Madej reports having received travel grants from Accord, BMS, MSD, and lecture fees from AstraZeneca, BMS, GSK, Novartis, Roche; Paweł Blecharz reports having received lecture fees, travel grants, and advisory board participation from AstraZeneca, GSK, Merck, AbbVie. Funding Not applicable Authors' contributions WSZ,MNJ,MM,TK,AK,MCS,MS,IW,JT,JJ,RPM,PB: design of the RONCODC study. WSZ,MNJ,MM,TK,AK,MCS,MS,IW,JT,JJ,RPM,PB :collection of data. WSZ,MNJ,PB took primary responsibility for writing the manuscript.All authors critically revised the manuscript and approved the final draft.WSZ is the corresponding author ( [email protected] ). Acknowledgements Not applicable References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660 . Siegel RL, Miller KD, Wagle NS, Jemal A, Cancer statistics. 2023. CA Cancer J Clin. 2023;73(1):17–48. 10.3322/caac.21722 Oaknin A, Bosse TJ, Creutzberg CL, Giornelli G, Harter P, Joly F, et al. Endometrial cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol. 2022;33(9):860–77. 10.1016/j.annonc.2022.07.005 . American Cancer Society. Cancer facts & Figs. 2022 [Internet]. Atlanta: American Cancer Society. 2022. Available from: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html . Accessed on 26 March 2025. Levine D, The Cancer Genome Atlas Research Network. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497(7447):67–73. 10.1038/nature12113 . Talhouk A, McConechy MK, Leung S, Yang W, Lum A, Senz J, et al. Confirmation of ProMisE: a simple, genomics-based clinical classifier for endometrial cancer. Cancer. 2017;123(5):802–13. 10.1002/cncr.30496 . Concin N, Matias-Guiu X, Vergote I, Cibula D, Mirza MR, Marnitz S, et al. ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int J Gynecol Cancer. 2021;31(1):12–39. 10.1136/ijgc-2020-002230 . León-Castillo A, Gilvazquez E, Nout R, Smit VTHBM, McAlpine JN, McConechy M, et al. Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas. J Pathol. 2020;250(3):312–22. 10.1002/path.5366 . De Vitis LA, Schivardi G, Caruso G, Fumagalli C, Vacirca D, Achilarre MT, et al. Clinicopathological characteristics of multiple-classifier endometrial cancers: a cohort study and systematic review. Int J Gynecol Cancer. 2024;34(2):229–38. 10.1136/ijgc-2023-004864 . Szatkowski W, Kluz T, Cieślak-Steć M, Śliwińska M, Nowak-Jastrząb M, Winkler I, et al. Analysis of differences in the classification of endometrial cancer patients in Poland. Cancers (Basel). 2025;17(2):213. 10.3390/cancers17020213 . Zhou Y, Tao L, Qiu J, Xu J, Yang X, Zhang Y, et al. Tumor biomarkers for diagnosis, prognosis and targeted therapy. Signal Transduct Target Ther. 2024;9(1):132. 10.1038/s41392-024-01738-4 . Nagel J, Paschoalini RB, Barreto PSD, Garcia TB, Santos MJ, Santos RS, et al. Predictive biomarkers in endometrial carcinomas: a review of their relevance in daily anatomic pathology. Surg Exp Pathol. 2024;7:21. 10.1186/s42047-024-00164-2 . Corr B, Cosgrove C, Spinosa D, Guntupalli S. Endometrial cancer: molecular classification and future treatments. BMJ Med. 2022;1(1):e000152. 10.1136/bmjmed-2022-000152 . Stelloo E, Nout RA, Osse EM, Jürgenliemk-Schulz IM, Jobsen JJ, Lutgens LC, et al. Improved risk assessment by integrating molecular and clinicopathological factors in early-stage endometrial cancer—combined analysis of the PORTEC cohorts. Clin Cancer Res. 2016;22(16):4215–24. 10.1158/1078-0432.CCR-16-0431 . Kommoss S, McConechy MK, Kommoss F, Leung S, Bunz A, Magrill J, et al. Final validation of the ProMisE molecular classifier for endometrial carcinoma in a large population-based case series. Ann Oncol. 2018;29(5):1180–8. 10.1093/annonc/mdy058 . Riedinger CJ, Esnakula A, Haight PJ, Suarez AA, Chen W, Gillespie J, et al. Characterization of mismatch-repair/microsatellite instability-discordant endometrial cancers. Cancer. 2024;130(3):385–99. 10.1002/cncr.35030 . Dedeurwaerdere F, Claes KB, Van Dorpe J, Rottiers I, Van Der Meulen J, Breyne J, et al. Comparison of microsatellite instability detection by immunohistochemistry and molecular techniques in colorectal and endometrial cancer. Sci Rep. 2021;11(1):12880. 10.1038/s41598-021-92092-7 . Kato MK, Fujii E, Asami Y, Momozawa Y, Hiranuma K, Komatsu M, et al. Clinical features and impact of p53 status on sporadic mismatch repair deficiency and Lynch syndrome in uterine cancer. Cancer Sci. 2024;115(5):1646–55. 10.1111/cas.16121 . Michalova K, Strakova-Peterikova A, Ondic O, Vanecek T, Michal M, Hejhalova N, et al. Next-generation sequencing in the molecular classification of endometrial carcinomas: experience with 270 cases suggesting a potentially more aggressive clinical behavior of multiple classifier endometrial carcinomas. Virchows Arch. 2024. 10.1007/s00428-024-03996-1 . Bogani G, Betella I, Multinu F, Casarin J, Ghezzi F, Sorbi F, et al. Characteristics and outcomes of surgically staged multiple classifier endometrial cancer. Eur J Surg Oncol. 2024;50(1):107269. 10.1016/j.ejso.2023.107269 . Mirza MR, Chase DM, Slomovitz BM, dePont Christensen R, Novák Z, Black D, et al. Dostarlimab for primary advanced or recurrent endometrial cancer. N Engl J Med. 2023;388(23):2145–58. 10.1056/NEJMoa2216334 . Additional Declarations Competing interest reported. The authors declare the following potential conflicts of interest: Wiktor Szatkowski reports having received lecture fees from AstraZeneca; Małgorzata Nowak-Jastrząb reports having received lecture fees and travel grants from AstraZeneca, MSD; Marcin Misiek reports no conflict of interest; Tomasz Kluz reports having received lecture fees and travel grants from AstraZeneca, GSK; Aleksandra Kmieć reports no conflict of interest; Małgorzata Cieślak-Steć reports having received lecture fees from AstraZeneca, GSK and participation in clinical trials for AstraZeneca, MSD, PSI, Medpace, Roche, Syneos Health; Magdalena Śliwińska reports no conflict of interest; Izabela Winkler reports no conflict of interest; Jacek Tomaszewski reports having received lecture fees from GSK, Gedeon Richter; Jerzy Jakubowicz reports no conflict of interest; Renata Pacholczak-Madej reports having received travel grants from Accord, BMS, MSD, and lecture fees from AstraZeneca, BMS, GSK, Novartis, Roche; Paweł Blecharz reports having received lecture fees, travel grants, and advisory board participation from AstraZeneca, GSK, Merck, AbbVie. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6538787","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452311798,"identity":"0d1fc83d-4e9c-4441-85b8-872b3ce821fa","order_by":0,"name":"Wiktor Szatkowski","email":"data:image/png;base64,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","orcid":"","institution":"Department of Gynaecologic Oncology Maria Sklodowska-Curie National Research Institute of Oncology Krakow Branch, 31-315 Krakow","correspondingAuthor":true,"prefix":"","firstName":"Wiktor","middleName":"","lastName":"Szatkowski","suffix":""},{"id":452311799,"identity":"79412edd-674d-428c-8deb-cab27455ffce","order_by":1,"name":"Małgorzata Nowak-Jastrząb","email":"","orcid":"","institution":"Department of Gynaecologic Oncology Maria Sklodowska-Curie National Research Institute of Oncology Krakow Branch, 31-315 Krakow","correspondingAuthor":false,"prefix":"","firstName":"Małgorzata","middleName":"","lastName":"Nowak-Jastrząb","suffix":""},{"id":452311801,"identity":"fb5149bc-8d47-4cea-8f6e-754472deb784","order_by":2,"name":"Marcin Misiek","email":"","orcid":"","institution":"Department of Gynecologic Oncology, Holy Cross Cancer Center, 25-734 Kielce","correspondingAuthor":false,"prefix":"","firstName":"Marcin","middleName":"","lastName":"Misiek","suffix":""},{"id":452311802,"identity":"cb730c8a-c5d5-408b-9727-7fabf1cdb660","order_by":3,"name":"Tomasz Kluz","email":"","orcid":"","institution":"Department of Gynecology and Obstetrics, Institute of Medical Sciences, Medical College of Rzeszow University, Rzeszów","correspondingAuthor":false,"prefix":"","firstName":"Tomasz","middleName":"","lastName":"Kluz","suffix":""},{"id":452311803,"identity":"9b83868a-2c1a-4ddf-9036-c7142279dfa1","order_by":4,"name":"Aleksandra Kmieć","email":"","orcid":"","institution":"Department of Gynecology and Obstetrics, Institute of Medical Sciences, Medical College of Rzeszow University, Rzeszów","correspondingAuthor":false,"prefix":"","firstName":"Aleksandra","middleName":"","lastName":"Kmieć","suffix":""},{"id":452311804,"identity":"57653e1b-728c-4198-838f-50d310bfd863","order_by":5,"name":"Małgorzata Cieślak-Steć","email":"","orcid":"","institution":"Department of III Clinic of Radiotherapy and ChemotherapyMaria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, 44-102 Gliwice","correspondingAuthor":false,"prefix":"","firstName":"Małgorzata","middleName":"","lastName":"Cieślak-Steć","suffix":""},{"id":452311805,"identity":"9e3c0c21-3351-4246-939e-0075f5c5cecf","order_by":6,"name":"Magdalena Śliwińska","email":"","orcid":"","institution":"Department of III Clinic of Radiotherapy and ChemotherapyMaria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, 44-102 Gliwice","correspondingAuthor":false,"prefix":"","firstName":"Magdalena","middleName":"","lastName":"Śliwińska","suffix":""},{"id":452311806,"identity":"aa8cf288-4935-4317-aa59-6811392aba12","order_by":7,"name":"Izabela Winkler","email":"","orcid":"","institution":"Second Department of Gynecological Oncology, St. John's Center of Oncology of the Lublin Region, 20-090 Lublin","correspondingAuthor":false,"prefix":"","firstName":"Izabela","middleName":"","lastName":"Winkler","suffix":""},{"id":452311807,"identity":"ca36ebef-82ef-4bc4-a419-d730a6a2054d","order_by":8,"name":"Jacek Tomaszewski","email":"","orcid":"","institution":"Second Department of Gynecological Oncology, St. John's Center of Oncology of the Lublin Region, 20-090 Lublin","correspondingAuthor":false,"prefix":"","firstName":"Jacek","middleName":"","lastName":"Tomaszewski","suffix":""},{"id":452311810,"identity":"a02fdc09-3618-4de2-9231-7b5ad9a04390","order_by":9,"name":"Jerzy Jakubowicz","email":"","orcid":"","institution":"Department of Gynaecologic Oncology Maria Sklodowska-Curie National Research Institute of Oncology Krakow Branch, 31-315 Krakow","correspondingAuthor":false,"prefix":"","firstName":"Jerzy","middleName":"","lastName":"Jakubowicz","suffix":""},{"id":452311811,"identity":"af3b389a-6bfe-4276-8501-656f9f0479fb","order_by":10,"name":"Renata Pacholczak-Madej","email":"","orcid":"","institution":"Department of Gynaecologic Oncology Maria Sklodowska-Curie National Research Institute of Oncology Krakow Branch, 31-315 Krakow","correspondingAuthor":false,"prefix":"","firstName":"Renata","middleName":"","lastName":"Pacholczak-Madej","suffix":""},{"id":452311812,"identity":"ae5f81ee-704a-4c65-b402-f7b343ebf502","order_by":11,"name":"Paweł Blecharz","email":"","orcid":"","institution":"Department of Gynaecologic Oncology Maria Sklodowska-Curie National Research Institute of Oncology Krakow Branch, 31-315 Krakow","correspondingAuthor":false,"prefix":"","firstName":"Paweł","middleName":"","lastName":"Blecharz","suffix":""}],"badges":[],"createdAt":"2025-04-27 07:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6538787/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6538787/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82177548,"identity":"757ad6da-2555-4d11-8817-2fc99d41dc95","added_by":"auto","created_at":"2025-05-07 11:21:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":173488,"visible":true,"origin":"","legend":"\u003cp\u003eHistotype Distribution by Molecular Subtype\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6538787/v1/3f0e98c34cfce4827482b779.jpeg"},{"id":82177549,"identity":"ff8fa9b6-4c29-471f-addb-f196d06dc21e","added_by":"auto","created_at":"2025-05-07 11:21:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162279,"visible":true,"origin":"","legend":"\u003cp\u003eGrade Distribution by Molecular Subtype\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6538787/v1/3788b3efd7e86d1881af4c2f.jpeg"},{"id":82177546,"identity":"f4f104ff-0ef9-48f4-88b2-2610cf4db5c5","added_by":"auto","created_at":"2025-05-07 11:21:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180294,"visible":true,"origin":"","legend":"\u003cp\u003eRisk Group Distribution by Molecular Subtype\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6538787/v1/5f7ee1902068e13c71e5797c.jpeg"},{"id":84405197,"identity":"68883f37-605c-40bd-8ad6-04afe28a2ce2","added_by":"auto","created_at":"2025-06-11 14:17:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1476103,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6538787/v1/c4883494-38d9-4bba-b1aa-73439014578c.pdf"}],"financialInterests":"Competing interest reported. The authors declare the following potential conflicts of interest: Wiktor Szatkowski reports having received lecture fees from AstraZeneca; Małgorzata Nowak-Jastrząb reports having received lecture fees and travel grants from AstraZeneca, MSD; Marcin Misiek reports no conflict of interest; Tomasz Kluz reports having received lecture fees and travel grants from AstraZeneca, GSK; Aleksandra Kmieć reports no conflict of interest; Małgorzata Cieślak-Steć reports having received lecture fees from AstraZeneca, GSK and participation in clinical trials for AstraZeneca, MSD, PSI, Medpace, Roche, Syneos Health; Magdalena Śliwińska reports no conflict of interest; Izabela Winkler reports no conflict of interest; Jacek Tomaszewski reports having received lecture fees from GSK, Gedeon Richter; Jerzy Jakubowicz reports no conflict of interest; Renata Pacholczak-Madej reports having received travel grants from Accord, BMS, MSD, and lecture fees from AstraZeneca, BMS, GSK, Novartis, Roche; Paweł Blecharz reports having received lecture fees, travel grants, and advisory board participation from AstraZeneca, GSK, Merck, AbbVie.","formattedTitle":"Multiple-classifier endometrial cancers: the first multicenter clinicopathological study from five Polish institutions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer (EC) is a major gynecological malignancy with a rising global burden, with 417,367 new cases and 97,370 deaths reported in 2020 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the United States, EC incidence increased by 40% from 2012 to 2022, driven by aging populations and rising prevalence of risk factors such as obesity and diabetes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While early-stage EC has favorable outcomes, metastatic disease carries a five-year survival rate of only 17% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], highlighting the need for improved risk stratification and personalized therapies.\u003c/p\u003e \u003cp\u003eThe Cancer Genome Atlas (TCGA) established four molecular subtypes of EC: POLEmut (ultramutated, best prognosis), MMRd (mismatch repair deficient, intermediate risk), p53abn (copy-number high, worst prognosis), and NSMP (copy-number low, good-to-intermediate prognosis) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Validated by the ProMisE system, these subtypes guide treatment decisions in ESGO/ESTRO/ESP 2021, ESMO, and FIGO 2023 guidelines [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, multiple-classifier ECs\u0026mdash;tumors exhibiting features of more than one subtype, such as MMRd-p53abn\u0026mdash;pose challenges due to their uncertain prognostic and therapeutic implications [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The MMRd-p53abn subgroup, in particular, raises questions about its intermediate (MMRd) versus high-risk (p53abn) status, with limited data on its clinicopathological characteristics and risk group assignment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, regional differences in EC molecular profiles remain underexplored, especially in Central Europe.\u003c/p\u003e \u003cp\u003eThis study, the first multi-center analysis of EC in Poland, aims to evaluate the prevalence and clinicopathological features of multiple-classifier ECs, with a focus on MMRd-p53abn, within the ESGO/ESTRO/ESP risk stratification framework. By addressing gaps in the literature regarding these complex subtypes and providing unique data from an underrepresented population, our findings contribute to refining risk stratification and informing future therapeutic strategies for EC management.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Population\u003c/h2\u003e \u003cp\u003eThis multi-center study enrolled 1205 consecutive endometrial cancer (EC) patients treated between April 2022 and December 2024 across five oncology centers in southeastern Poland (Silesia, Lesser Poland, Subcarpathia, Holy Cross, and Lublin regions). Molecular profiling was performed as part of routine clinical practice following surgical treatment, with clinicopathological features (histotype, grade, myometrial invasion, lymphovascular space invasion (LVSI) assessed per ESGO/ESTRO/ESP 2021 guidelines. Patients were categorized into molecular subtypes (MMRd, p53abn, POLEmut, NSMP) based on immunohistochemistry (IHC) and sequencing results, using a surrogate classification approach consistent with the ProMisE system. Multiple-classifier ECs were identified when tumors exhibited features of more than one molecular subtype.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2. Immunohistochemistry (IHC) Analysis\u003c/h3\u003e\n\u003cp\u003eIHC analysis of mismatch repair (MMR) proteins (MLH1, MSH2, MSH6, PMS2) and p53 was conducted uniformly across centers using OptiView and UltraView kits (Ventana, Roche Diagnostics, Indianapolis, IN, USA) on the BenchMark Ultra system, as described previously [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. MMR deficiency (MMRd) was defined as loss of expression of at least one MMR protein; p53 abnormality (p53abn) was classified as a mutant or null staining pattern.\u003c/p\u003e\n\u003ch3\u003e2.3. DNA Extraction and Molecular Analysis\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissues using QIAamp DSP DNA FFPE Tissue Kit (Qiagen, Hilden, Germany) or Maxwell\u0026reg; RSC DNA FFPE Kit (Promega, Madison, WI, USA), following methods adapted from Szatkowski et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. POLE mutations were assessed by Sanger sequencing of exons 9, 11, 13, and 14, with pathogenicity classified per Le\u0026oacute;n-Castillo et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Next-generation sequencing (NGS) on the IonTorrent platform, using a custom panel (Thermo Fisher Scientific Ion AmpliSeq Designer), targeted MLH1, MSH2, MSH6, PMS2, POLE, and TP53.\u003c/p\u003e\n\u003ch3\u003e2.4. Variant Classification\u003c/h3\u003e\n\u003cp\u003eGenetic variants were classified using ClinVar, OncoKB, and Varsome databases as pathogenic, likely pathogenic, variants of unknown significance (VUS), likely benign, or benign. Only pathogenic and likely pathogenic variants were considered relevant due to their established clinical significance.\u003c/p\u003e\n\u003ch3\u003e2.5. Standardization\u003c/h3\u003e\n\u003cp\u003eTo ensure consistency across centers, IHC for MMR and p53, and NGS for POLE and TP53, followed identical protocols and panels, with uniform clinicopathological criteria, building on Szatkowski et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 27 (IBM Corp., Armonk, NY, USA). Group comparisons used chi-square or Fisher\u0026rsquo;s exact tests for categorical variables and Student\u0026rsquo;s t-test or Mann-Whitney U test for continuous variables. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.7. Ethical Considerations\u003c/h3\u003e\n\u003cp\u003eThe study adhered to the Declaration of Helsinki and was approved by the Institutional Review Board of the Maria Skłodowska-Curie National Research Institute of Oncology, Krakow Branch, Poland (Approval No. 6/2025, issued on 9 January 2025). Although this study was retrospective and involved anonymized data, additional ethical approval was sought and granted to ensure compliance with the highest standards of ethical research practices.\u003c/p\u003e \u003cp\u003eWritten informed consent was obtained from all subjects at the start of treatment. The consent included explicit permission to use their medical data for retrospective analysis and research purposes. All patient data were anonymized prior to analysis, in strict adherence to GDPR regulations, ensuring the confidentiality and privacy of all participants.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Prevalence of Multiple-Classifier Tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 1205 patients, multiple-classifier endometrial cancers (ECs) accounted for 7.5% (90/1205), including MMRd-p53abn (4.2%, 51/1205), POLEmut-p53abn (0.3%, 4/1205), POLEmut-MMRd (1.9%, 23/1205), and POLEmut-MMRd-p53abn (1.0%, 12/1205). This prevalence is lower than the 11.4% reported by De Vitis et al.[9], which may be attributed to differences in diagnostic criteria or population characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. MMRd, p53abn, and MMRd-p53abn Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e presents the clinicopathological characteristics of MMRd, p53abn, and MMRd-p53abn tumors. Compared to classical MMRd (N=280), MMRd-p53abn tumors (N=51) demonstrated:\u003cbr\u003e\u0026nbsp;\u0026bull; Higher rates of non-endometrioid histology (9.8% vs 2.86%, p=0.035).\u003cbr\u003e\u0026nbsp;\u0026bull; More frequent high-grade (G3) tumors (27.45% vs 12.14%, p=0.005).\u003cbr\u003e\u0026nbsp;\u0026bull; Greater proportion assigned to high-intermediate or high-risk (HIR/HR) groups (62.75% vs 37.50%,p=0.001).\u003cbr\u003eIn contrast, compared to p53abn tumors (N=174), MMRd-p53abn tumors exhibited a significantly lower proportion of non-endometrioid histology (9.8% vs 35.63%, p=0.001). These comparisons are visually represented in \u003cstrong\u003eFigure 1\u003c/strong\u003e (Histotype comparison), \u003cstrong\u003eFigure 2\u003c/strong\u003e (Grade comparison), and \u003cstrong\u003eFigure 3\u003c/strong\u003e (Risk group comparison). Compared to MMRd, MMRd-p53abn demonstrated higher odds of non-endometrioid histology (OR=3.70, 95% CI: 1.15\u0026ndash;11.89, p=0.035), high-grade (G3) tumors (OR=2.96, 95% CI: 1.45\u0026ndash;6.05, p=0.005), and classification into high-intermediate or high-risk (HIR/HR) groups (OR=2.81, 95% CI: 1.50\u0026ndash;5.25, p=0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. POLEmut and Multiple-Classifier POLEmut Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e compares classical POLEmut (N=34) with multiple-classifier POLEmut tumors. POLEmut-p53abn (N=4) exhibited more G3 tumors (75.00% vs 5.88%, p=0.005) and advanced FIGO stages III-IV (75.00% vs 5.88%, p=0.005) than POLEmut, despite limited cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Comparison with De Vitis et al.[9]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e summarizes the prevalence of multiple-classifier ECs from our cohort and the De Vitis et al.[9] study. The proportion of MMRd-p53abn was similar between the two studies (4.2% vs 6.6%). However, POLEmut-p53abn was less frequent in our cohort (0.3% vs 3.6%), while POLEmut-MMRd-p53abn was more common (1.0% vs 0.7%) [9]. This comparison suggests potential differences in classification criteria or patient population.\u003c/p\u003e\n\u003cp\u003eTable 1. Clinicopathological Characteristics of Patients with Endometrial Cancer by Molecular Subtype\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eMMRd (A) (N=280)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003ep53abn (B) (N=174)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eMMRd-p53abn (C) (N=51)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003ep-value (A vs C)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eOR (95% CI, A vs C)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003ep-value (B vs C)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eMMRd + MMRd-p53abn (N=331)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eAge at surgery (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e66.05 (9.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e68.19 (9.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e67.81 (11.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.718\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e66.32 (9.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\"\u003e\n\u003cp\u003eAge at surgery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026lt; 60 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e77 (27.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e31 (17.82%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e14 (27.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.124\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e91 (27.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e60\u0026ndash;70 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e99 (35.36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e58 (33.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e11 (21.57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e110 (33.23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026gt; 70 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e103 (36.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e83 (47.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e25 (49.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e128 (38.67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (0.36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2 (1.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (1.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2 (0.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003eHistology\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eEndometrioid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e272 (97.14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e112 (64.37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e46 (90.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.035*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e3.70 (1.15\u0026ndash;11.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e318 (96.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eNon-endometrioid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e8 (2.86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e62 (35.63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e5 (9.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e13 (3.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eGrade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eG1\u0026ndash;G2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e244 (87.14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e95 (54.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e34 (66.67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.005*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.96 (1.45\u0026ndash;6.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e278 (83.99%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eG3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e34 (12.14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e55 (31.61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e14 (27.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e48 (14.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2 (0.71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e24 (13.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e3 (5.88%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e5 (1.51%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eLVSI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eAbsent or focal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e211 (75.36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e110 (63.22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e30 (58.82%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.818\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e241 (72.81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eDiffuse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e68 (24.29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e61 (35.06%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e19 (37.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e87 (26.28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (0.36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e3 (1.72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2 (3.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e3 (0.91%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eMyometrial invasion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026lt; 1/2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e156 (55.71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e90 (51.72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e21 (41.18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e177 (53.47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u0026ge; 1/2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e122 (43.57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e83 (47.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e29 (56.86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e151 (45.62%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2 (0.71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (0.57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (1.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e3 (0.91%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eCervical involvement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e205 (73.21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e116 (66.67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e33 (64.71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.359\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e238 (71.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e74 (26.43%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e58 (33.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e17 (33.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e91 (27.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (0.36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0 (0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (1.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2 (0.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eLymph node metastases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e237 (84.64%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e138 (79.31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e38 (74.51%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.721\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e275 (83.08%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e17 (6.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e19 (10.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e7 (13.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e24 (7.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e26 (9.29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e17 (9.77%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e6 (11.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e32 (9.67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eDistant metastases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e224 (80.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e124 (71.26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e37 (72.55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e261 (78.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e3 (1.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e9 (5.17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0 (0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e3 (0.91%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e53 (18.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e41 (23.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e14 (27.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e67 (20.24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\"\u003e\n\u003cp\u003eFIGO stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eEarly (I\u0026ndash;II)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e237 (84.64%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e127 (72.99%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e38 (74.51%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e275 (83.08%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eAdvanced (III\u0026ndash;IV)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e43 (15.36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e46 (26.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e12 (23.53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e55 (16.62%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0 (0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (0.57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (1.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1 (0.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\"\u003e\n\u003cp\u003eRisk group**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eLow or Intermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e175 (62.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e49 (28.16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e19 (37.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.81 (1.50\u0026ndash;5.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e194 (58.61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eHigh-Intermediate and Higher\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e105 (37.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e122 (70.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e32 (62.75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e137 (41.39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Abbreviations: MMRd = mismatch repair deficient; p53abn = p53 abnormal; LVSI = lymphovascular space invasion; FIGO = International Federation of Gynecology and Obstetrics; SD = standard deviation; OR = odds ratio; CI = confidence interval.\u003cbr\u003e\u0026nbsp;Statistical significance: *p \u0026lt; 0.05.\u003cbr\u003e\u0026nbsp;Columns: MMRd (A): Classical MMRd tumors; p53abn (B): Classical p53abn tumors; MMRd-p53abn (C): Multiple-classifier MMRd-p53abn tumors; MMRd + MMRd-p53abn: Combined MMRd and MMRd-p53abn cases.\u003cbr\u003e\u0026nbsp;p-values: Calculated for comparisons between MMRd vs MMRd-p53abn and p53abn vs MMRd-p53abn.\u003cbr\u003e\u0026nbsp;OR (95% CI): Calculated for comparisons with p\u0026lt;0.05 (MMRd vs MMRd-p53abn) using chi-square or Fisher\u0026rsquo;s exact test.\u0026nbsp;\u003cbr\u003e\u0026nbsp;**ESGO/ESTRO/ESP 2020 guidelines applied; molecular classification per ProMisE.\u003c/p\u003e\n\u003cp\u003eTable 2. Comparison of POLEmut and Multiple-Classifier POLEmut Tumors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePOLEmut (N=34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePOLEmut-MMRd (N=23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePOLEmut-p53abn (N=4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePOLEmut-MMRd-p53abn (N=12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ep-value (POLEmut vs POLEmut-MMRd)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ep-value (POLEmut vs POLEmut-p53abn)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ep-value (POLEmut vs POLEmut-MMRd-p53abn)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGrade G3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2 (5.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4 (17.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3 (75.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLymph node metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1 (2.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1 (4.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1 (25.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFIGO stage III\u0026ndash;IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2 (5.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2 (8.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3 (75.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4 (33.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.033*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes:\u003c/p\u003e\n\u003cp\u003eAbbreviations: POLEmut = POLE ultramutated; MMRd = mismatch repair deficient; p53abn = p53 abnormal; FIGO = International Federation of Gynecology and Obstetrics.\u003c/p\u003e\n\u003cp\u003eStatistical significance: *p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eData presentation: Values are presented as n (%) unless otherwise specified.\u003c/p\u003e\n\u003cp\u003ep-values: Calculated for comparisons between POLEmut and each multiple-classifier subgroup.\u003c/p\u003e\n\u003cp\u003eTable 3. Comparison of Multiple-Classifier Endometrial Cancers: Current Study vs De Vitis et al.[10]\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eTumor Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eCurrent Study (N=1205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eDe Vitis et al. (2024) (N=422)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMMRd-p53abn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e4.2% (51/1205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e6.6% (28/422)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePOLEmut-p53abn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e0.3% (4/1205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e3.6% (15/422)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePOLEmut-MMRd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e1.9% (23/1205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e0.5% (2/422)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePOLEmut-MMRd-p53abn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e1.0% (12/1205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e0.7% (3/422)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes:\u003c/p\u003e\n\u003cp\u003eAbbreviations: MMRd = mismatch repair deficient; POLEmut = POLE ultramutated; p53abn = p53 abnormal.\u003c/p\u003e\n\u003cp\u003eData presentation: Values are presented as percentage (%) followed by the number of cases (n/N).\u003cbr\u003e\u0026nbsp;Source: De Vitis et al.[9] data are cited for comparative purposes\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1. Significance of Results in the Context of Literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study aimed to evaluate the clinical and pathological characteristics of multiple-classifier endometrial cancers, particularly focusing on the MMRd-p53abn subtype, to better understand their prognostic implications. The advent of molecular classification has transformed our understanding of endometrial cancer (EC) biology [11,12,13]. In our study, multiple-classifier ECs comprised 7.5% of cases (90/1205), consistent with the reported range of 3\u0026ndash;11% [14,15,9]. This detection rate may reflect the application of advanced diagnostics, such as next-generation sequencing (NGS) and detailed immunohistochemical (IHC) analysis of MMR and p53 proteins [16,17].\u003cbr\u003e\u0026nbsp; Multiple-classifier ECs with POLEmut are typically associated with favorable prognosis, even when co-occurring with MMRd/MSI-H and/or p53abn, as the ultramutated POLE profile appears to override other classifiers\u0026rsquo; prognostic impact [5;8]. However, our analysis focuses on MMRd-p53abn, the most common multiple-classifier subtype in our cohort (4.2%, 51/1205), highlighting the importance of better characterizing its clinical behavior and risk classification.\u003cbr\u003e\u0026nbsp; Particular focus is placed on the MMRd-p53abn subgroup, whose classification remains debated. In the ProMisE system, MMRd tumors are deemed intermediate-risk [6]. However, our results demonstrate a more aggressive phenotype for MMRd-p53abn compared to classical MMRd, with increased non-endometrioid histology (9.8% vs 2.86%, p=0.035), higher prevalence of high-grade tumors (27.45% vs 12.14%, p=0.005), and greater assignment to risk groups above high-intermediate risk (HIR) (62.75% vs 37.50%, p=0.001). Still, they are less adverse than p53abn, as evidenced by a lower rate of non-endometrioid histology (9.8% vs 35.63%,p=0.001).\u003cbr\u003e\u0026nbsp; Comparison with Le\u0026oacute;n-Castillo et al. [8] highlights discrepancies between our findings. While their analysis of 1031 cases suggested that MMRd-p53abn should be treated similarly to MMRd in terms of prognosis, our results indicate a distinct subgroup with more aggressive features. This difference may be due to a lack of survival data in our cohort, but it aligns with reports from Kato et al.[18], Michalova et al.[19], and De Vitis et al.[9], which document a more adverse clinical profile for MMRd-p53abn.\u003cbr\u003e\u0026nbsp; In particular, De Vitis et al.[9] reported a prevalence of 6.6% for MMRd-p53abn within a cohort of 422 patients, higher than our findings (4.2%). The variation may be due to differences in diagnostic criteria, population characteristics, or methodological factors. Nonetheless, the clinicopathological profile described by De Vitis et al.[9] is largely consistent with our findings, including associations with high-grade tumors and more advanced stages.\u003cbr\u003eThe observed differences in MMRd-p53abn prevalence and characteristics compared to De Vitis et al.[9] and Le\u0026oacute;n-Castillo et al.[8] may partly stem from variations in molecular profiling techniques. Our study utilized a surrogate classification combining immunohistochemistry (IHC) for MMR and p53 with targeted sequencing (Sanger sequencing for POLE mutations and limited NGS panels), whereas other studies may have employed broader NGS panels or different IHC thresholds for defining classifier overlap. These methodological variations underscore the need for standardized molecular profiling protocols to enhance comparability across studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp; Clinical Implications The ESGO/ESTRO/ESP 2021 guidelines and ProMisE\u0026nbsp;\u003cbr\u003e\u0026nbsp;classification categorize MMRd-p53abn as MMRd, potentially underestimating their aggressive potential. Our data show MMRd-p53abn are more frequently assigned to HIR and high-risk (HR) groups (62.75%) than classical MMRd (37.50%), suggesting these patients may require more intensive adjuvant therapy than standard MMRd protocols. This is supported by Kato et al.[18] and Michalova et al.[19], who note features closer to p53abn, prompting consideration of HR reclassification. De Vitis et al.[9] also advocate for tailored therapeutic approaches given their aggressive clinicopathological profile.\u003cbr\u003eUnlike Le\u0026oacute;n-Castillo et al.[8], our study is based on detailed clinicopathological analysis rather than long-term survival data. However, our findings are consistent with the observations of Bogani et al.[20], who reported that MMRd-p53abn patients experienced increased risk of recurrence despite adjuvant treatment. If further studies confirm these findings, an independent risk category for MMRd-p53abn could be justified, potentially influencing treatment strategies such as radiotherapy or targeted therapies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.\u003c/strong\u003e\u003cbr\u003eNeed for Further Research Our study reveals distinct clinicopathological differences between MMRd and MMRd-p53abn, yet the absence of OS and PFS data limits prognostic conclusions. Further studies should determine if MMRd-p53abn warrant HR classification and more aggressive therapies than classical MMRd. The divergence from Le\u0026oacute;n-Castillo et al. [8] underscores the need for validation through prospective trials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.\u003c/strong\u003e\u0026nbsp;\u003cbr\u003eNovel Therapeutic Approaches and Study Implications Immunotherapy, such as dostarlimab in GARNET and RUBY trials [21], and combination regimens (PD-1 inhibitors + PARP inhibitors) may offer potential benefits for selected MMRd-p53abn patients (Mirza et al.[21]). However, their efficacy in these specific multiple-classifier subtypes awaits confirmation through clinical trials with long-term OS and PFS assessment. These therapeutic advances could be particularly relevant for MMRd-p53abn patients, given their aggressive clinicopathological profile identified in our cohort.\u003cbr\u003eAs the first multi-center study in Poland, encompassing 1205 patients across five institutions, our analysis offers a unique contribution to the literature. The MMRd-p53abn frequency (4.2%) differs from other populations (e.g., 6.6% in De Vitis et al.) [9], potentially reflecting cohort-specific characteristics, which merits further exploration. These findings could inform future clinical guidelines and personalized treatment strategies for endometrial cancer patients with complex molecular profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5.\u003c/strong\u003e\u003cbr\u003eLimitations. This study has several limitations. The absence of overall survival (OS) and progression-free survival (PFS) data limits the ability to draw conclusions about the prognostic implications of multiple-classifier endometrial cancers, with findings relying on clinicopathological surrogates such as histology, grade, and FIGO stage. Small subgroup sizes, particularly for POLEmut-p53abn (N=4) and POLEmut-MMRd-p53abn (N=12), restrict the reliability of statistical inferences, necessitating cautious interpretation and validation in larger cohorts. The lack of multivariate analysis to adjust for potential confounders, such as age, FIGO stage, or lymphovascular space invasion, limits the assessment of the independent impact of molecular subtypes. Additionally, detailed data on lost mismatch repair (MMR) protein pairs (e.g., MLH1/PMS2, MSH2/MSH6) were not available, restricting the characterization of MMRd subtypes. The heterogeneity of secondary p53 alterations (e.g., clonal vs subclonal) was not assessed due to limited molecular data, which may affect the understanding of multiple-classifier tumor behavior. Detailed concordance between IHC, Sanger sequencing, and next-generation sequencing (NGS) results was not evaluated, as it was beyond the scope of this study. Further prospective studies with comprehensive molecular and clinical data are needed to validate these findings and assess their therapeutic significance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEC- Endometrial cancer\u003c/p\u003e\n\u003cp\u003ePOLEmut -\u003cem\u003ePOLE\u003c/em\u003e ultramutated\u003c/p\u003e\n\u003cp\u003eMMRd -mismatch repair deficient\u003c/p\u003e\n\u003cp\u003ep53abn -p53 abnormal\u003c/p\u003e\n\u003cp\u003eNSMP-no specific molecular profile\u003c/p\u003e\n\u003cp\u003eHIR/HR- high-risk\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eG3- high-grade\u003c/p\u003e\n\u003cp\u003eTCGA- The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eLVSI- lymphovascular space invasion\u003c/p\u003e\n\u003cp\u003eIHC- immunohistochemistry\u003c/p\u003e\n\u003cp\u003eNGS- Next-generation sequencing\u003c/p\u003e\n\u003cp\u003eVUS- variants of unknown significance\u003c/p\u003e\n\u003cp\u003eOS-overall survival\u003c/p\u003e\n\u003cp\u003ePFS- progression free survival\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Maria Skłodowska-Curie National Research Institute of Oncology (NIO-PIB), Krak\u0026oacute;w Branch, Poland (Approval No. 6/2025, issued on 9 January 2025). Although this study was retrospective and involved anonymized data, additional ethical approval was sought and granted to ensure compliance with the highest standards of ethical research practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the start of the treatment, written informed consent was obtained from all subjects involved in this study. The consent included explicit permission to use their medical data for retrospective analysis and research purposes. All patient data were anonymized prior to analysis, in strict adherence to GDPR regulations, ensuring the confidentiality and privacy of all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following potential conflicts of interest: Wiktor Szatkowski reports having received lecture fees from AstraZeneca; Małgorzata Nowak-Jastrząb reports having received lecture fees and travel grants from AstraZeneca, MSD; Marcin Misiek reports no conflict of interest; Tomasz Kluz reports having received lecture fees and travel grants from AstraZeneca, GSK; Aleksandra Kmieć reports no conflict of interest; Małgorzata Cieślak-Steć reports having received lecture fees from AstraZeneca, GSK and participation in clinical trials for AstraZeneca, MSD, PSI, Medpace, Roche, Syneos Health; Magdalena Śliwińska reports no conflict of interest; Izabela Winkler reports no conflict of interest; Jacek Tomaszewski reports having received lecture fees from GSK, Gedeon Richter; Jerzy Jakubowicz reports no conflict of interest; Renata Pacholczak-Madej reports having received travel grants from Accord, BMS, MSD, and lecture fees from AstraZeneca, BMS, GSK, Novartis, Roche; Paweł Blecharz reports having received lecture fees, travel grants, and advisory board participation from AstraZeneca, GSK, Merck, AbbVie.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWSZ,MNJ,MM,TK,AK,MCS,MS,IW,JT,JJ,RPM,PB: design of the RONCODC study. WSZ,MNJ,MM,TK,AK,MCS,MS,IW,JT,JJ,RPM,PB :collection of data. WSZ,MNJ,PB took primary responsibility for writing the manuscript.All authors critically revised the manuscript and approved the final draft.WSZ is the corresponding author ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21660\u003c/span\u003e\u003cspan address=\"10.3322/caac.21660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A, Cancer statistics. 2023. CA Cancer J Clin. 2023;73(1):17\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21722\u003c/span\u003e\u003cspan address=\"10.3322/caac.21722\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOaknin A, Bosse TJ, Creutzberg CL, Giornelli G, Harter P, Joly F, et al. Endometrial cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol. 2022;33(9):860\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.annonc.2022.07.005\u003c/span\u003e\u003cspan address=\"10.1016/j.annonc.2022.07.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Cancer Society. Cancer facts \u0026amp; Figs. 2022 [Internet]. Atlanta: American Cancer Society. 2022. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html\u003c/span\u003e\u003cspan address=\"https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed on 26 March 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine D, The Cancer Genome Atlas Research Network. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497(7447):67\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature12113\u003c/span\u003e\u003cspan address=\"10.1038/nature12113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalhouk A, McConechy MK, Leung S, Yang W, Lum A, Senz J, et al. Confirmation of ProMisE: a simple, genomics-based clinical classifier for endometrial cancer. Cancer. 2017;123(5):802\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.30496\u003c/span\u003e\u003cspan address=\"10.1002/cncr.30496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConcin N, Matias-Guiu X, Vergote I, Cibula D, Mirza MR, Marnitz S, et al. ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int J Gynecol Cancer. 2021;31(1):12\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/ijgc-2020-002230\u003c/span\u003e\u003cspan address=\"10.1136/ijgc-2020-002230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe\u0026oacute;n-Castillo A, Gilvazquez E, Nout R, Smit VTHBM, McAlpine JN, McConechy M, et al. Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas. J Pathol. 2020;250(3):312\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/path.5366\u003c/span\u003e\u003cspan address=\"10.1002/path.5366\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Vitis LA, Schivardi G, Caruso G, Fumagalli C, Vacirca D, Achilarre MT, et al. Clinicopathological characteristics of multiple-classifier endometrial cancers: a cohort study and systematic review. Int J Gynecol Cancer. 2024;34(2):229\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/ijgc-2023-004864\u003c/span\u003e\u003cspan address=\"10.1136/ijgc-2023-004864\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzatkowski W, Kluz T, Cieślak-Steć M, Śliwińska M, Nowak-Jastrząb M, Winkler I, et al. Analysis of differences in the classification of endometrial cancer patients in Poland. Cancers (Basel). 2025;17(2):213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers17020213\u003c/span\u003e\u003cspan address=\"10.3390/cancers17020213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Tao L, Qiu J, Xu J, Yang X, Zhang Y, et al. Tumor biomarkers for diagnosis, prognosis and targeted therapy. Signal Transduct Target Ther. 2024;9(1):132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-024-01738-4\u003c/span\u003e\u003cspan address=\"10.1038/s41392-024-01738-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagel J, Paschoalini RB, Barreto PSD, Garcia TB, Santos MJ, Santos RS, et al. Predictive biomarkers in endometrial carcinomas: a review of their relevance in daily anatomic pathology. Surg Exp Pathol. 2024;7:21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s42047-024-00164-2\u003c/span\u003e\u003cspan address=\"10.1186/s42047-024-00164-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorr B, Cosgrove C, Spinosa D, Guntupalli S. Endometrial cancer: molecular classification and future treatments. BMJ Med. 2022;1(1):e000152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjmed-2022-000152\u003c/span\u003e\u003cspan address=\"10.1136/bmjmed-2022-000152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStelloo E, Nout RA, Osse EM, J\u0026uuml;rgenliemk-Schulz IM, Jobsen JJ, Lutgens LC, et al. Improved risk assessment by integrating molecular and clinicopathological factors in early-stage endometrial cancer\u0026mdash;combined analysis of the PORTEC cohorts. Clin Cancer Res. 2016;22(16):4215\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-16-0431\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-16-0431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKommoss S, McConechy MK, Kommoss F, Leung S, Bunz A, Magrill J, et al. Final validation of the ProMisE molecular classifier for endometrial carcinoma in a large population-based case series. Ann Oncol. 2018;29(5):1180\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/annonc/mdy058\u003c/span\u003e\u003cspan address=\"10.1093/annonc/mdy058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiedinger CJ, Esnakula A, Haight PJ, Suarez AA, Chen W, Gillespie J, et al. Characterization of mismatch-repair/microsatellite instability-discordant endometrial cancers. Cancer. 2024;130(3):385\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.35030\u003c/span\u003e\u003cspan address=\"10.1002/cncr.35030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDedeurwaerdere F, Claes KB, Van Dorpe J, Rottiers I, Van Der Meulen J, Breyne J, et al. Comparison of microsatellite instability detection by immunohistochemistry and molecular techniques in colorectal and endometrial cancer. Sci Rep. 2021;11(1):12880. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-92092-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-92092-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato MK, Fujii E, Asami Y, Momozawa Y, Hiranuma K, Komatsu M, et al. Clinical features and impact of p53 status on sporadic mismatch repair deficiency and Lynch syndrome in uterine cancer. Cancer Sci. 2024;115(5):1646\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/cas.16121\u003c/span\u003e\u003cspan address=\"10.1111/cas.16121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichalova K, Strakova-Peterikova A, Ondic O, Vanecek T, Michal M, Hejhalova N, et al. Next-generation sequencing in the molecular classification of endometrial carcinomas: experience with 270 cases suggesting a potentially more aggressive clinical behavior of multiple classifier endometrial carcinomas. Virchows Arch. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00428-024-03996-1\u003c/span\u003e\u003cspan address=\"10.1007/s00428-024-03996-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBogani G, Betella I, Multinu F, Casarin J, Ghezzi F, Sorbi F, et al. Characteristics and outcomes of surgically staged multiple classifier endometrial cancer. Eur J Surg Oncol. 2024;50(1):107269. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejso.2023.107269\u003c/span\u003e\u003cspan address=\"10.1016/j.ejso.2023.107269\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirza MR, Chase DM, Slomovitz BM, dePont Christensen R, Nov\u0026aacute;k Z, Black D, et al. Dostarlimab for primary advanced or recurrent endometrial cancer. N Engl J Med. 2023;388(23):2145\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa2216334\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2216334\" 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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endometrial cancer, multiple-classifier tumors, MMRd, p53abn, POLEmut, molecular classification, risk stratification, clinicopathological characteristics","lastPublishedDoi":"10.21203/rs.3.rs-6538787/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6538787/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eEndometrial cancer (EC) exhibits molecular heterogeneity, classified into four subtypes: POLEmut (POLE ultramutated), MMRd (mismatch repair deficient), p53abn (p53 abnormal), and NSMP (no specific molecular profile). Tumors with multiple classifiers (multiple-classifier ECs) challenge risk stratification, particularly MMRd-p53abn and POLEmut-related subtypes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThis study aims to assess the prevalence and clinicopathological features of multiple-classifier ECs, focusing on MMRd-p53abn versus classical MMRd and p53abn, and their ESGO/ESTRO/ESP risk group alignment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe analyzed 1205 EC patients from five Polish oncology centers, molecularly profiled post-surgery. Subtypes included MMRd, p53abn, POLEmut, and multiple-classifier groups (MMRd-p53abn, POLEmut-p53abn, POLEmut-MMRd, POLEmut-MMRd-p53abn). Histotype, grade, myometrial invasion, lymphovascular space invasion, and risk groups classification were assessed. Multiple-classifier ECs comprised 7.5% (90/1205), with MMRd-p53abn at 4.2% (51/1205), POLEmut-p53abn 0.3% (4/1205), POLEmut-MMRd 1.9% (23/1205), and POLEmut-MMRd-p53abn 1.0% (12/1205). MMRd-p53abn tumors showed more non-endometrioid histology (9.8% vs 2.86%, p=0.035), high-grade (G3) tumors (27.45% vs 12.14%, p=0.005), and assignment to high-intermediate or high-risk (HIR/HR) groups (62.75% vs 37.50%, p=0.001) than MMRd, but less non-endometrioid histology than p53abn (9.8% vs 35.63%, p=0.001). POLEmut-p53abn tumors had higher G3 (75.00% vs 5.88%, p=0.005) and more FIGO III-IV stages (75.00% vs 5.88%, p=0.005) than POLEmut.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eMMRd-p53abn tumors are more aggressive than MMRd yet less adverse than p53abn, while POLEmut-p53abn diverge from POLEmut’s favorable prognosis. Their frequent assignment to high-intermediate or high-risk (HIR/HR) groups suggests refined risk stratification is needed. As Poland’s first multi-center EC study, these data may inform future guidelines.\u003c/p\u003e","manuscriptTitle":"Multiple-classifier endometrial cancers: the first multicenter clinicopathological study from five Polish institutions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 11:21:05","doi":"10.21203/rs.3.rs-6538787/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e50a2a9-f611-4da4-955c-084ca45e4215","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-11T14:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 11:21:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6538787","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6538787","identity":"rs-6538787","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}