Association Between Tumor Mutation Burden Status and Mismatch Repair Genes in Gynecologic Malignancies

Association Between Tumor Mutation Burden Status and Mismatch Repair Genes in Gynecologic Malignancies | 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 Association Between Tumor Mutation Burden Status and Mismatch Repair Genes in Gynecologic Malignancies MANABU SEINO, Shiori Sano, Yuka Tachibana, Shota Horikawa, Yasufumi Ito, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9354189/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Background: This study investigated the relationship between tumor mutation burden (TMB) status and mismatch repair (MMR) gene variants in gynecologic malignancies using cancer gene panel testing data in Japan. Methods: We analyzed data from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) for cases tested between March 2018 and June 2023. A total of 317 cases of endometrioid endometrial carcinoma (EEC), 669 cases of cervical squamous cell carcinoma (CSCC), and 32 cases of vaginal or vulvar squamous cell carcinoma (V/VSCC) were included. TMB-high was defined as ≥10 mutations per megabase (Muts/Mb). The detection rates of MMR gene variants ( MSH2, MSH3, MSH6, MLH1 , and PMS2 ) were compared between TMB-high and TMB-not-high groups. Results: In the EEC cohort, approximately 80% of TMB-high tumors were also classified as MSI-high, demonstrating a strong overlap between these biomarkers. TMB-high tumors showed increased frequencies of MMR gene variants, particularly in MSH3 and MSH6 . However, given the substantial concordance between TMB-high and MSI-high status, this enrichment likely reflects mutation accumulation driven by MSI-associated hypermutation rather than independent oncogenic effects of specific MMR gene alterations. In contrast, in CSCC and V/VSCC, the association between MMR gene variants and TMB-high status was limited and less pronounced. Conclusion: In this nationwide cohort, TMB-high status in gynecologic malignancies—especially in EEC—largely overlaps with MSI-high status. The enrichment of MMR gene variants in TMB-high tumors appears to be strongly influenced by MSI-driven hypermutation, highlighting the importance of interpreting MMR alterations within the context of MSI status. Figures Figure 1 Figure 2 Introduction The incidence of gynecologic malignancies has been increasing globally, presenting a significant challenge in clinical oncology [1]. In particular, the management of advanced-stage gynecologic cancers remains difficult due to limited therapeutic options and poor prognoses. Recently, comprehensive genomic profiling (CGP) has gained traction in clinical practice, particularly among patients with advanced or treatment-refractory malignancies [2]. This approach enables the identification of actionable mutations and facilitates personalized, targeted therapeutic strategies. However, several questions remain regarding the clinical utility and interpretability of CGP in gynecologic malignancies [3]. Among emerging molecular biomarkers, immune checkpoint inhibitors (ICIs) have demonstrated promising efficacy across various tumor types [4], especially in those exhibiting microsatellite instability-high (MSI-H) or tumor mutational burden-high (TMB-H) phenotypes [5]. TMB-H, defined as a high number of somatic mutations per megabase, has been proposed as a predictive marker for ICI responsiveness [6]. While environmental factors such as tobacco exposure and ultraviolet radiation are known contributors to elevated TMB, increasing evidence highlights the critical role of mismatch repair (MMR) gene deficiencies in hypermutated tumor pathogenesis [7]. Despite extensive research in colorectal cancer and endometrial cancer, where MMR deficiency is well established as a driver of hypermutation [8], comprehensive analyses of the relationship between MMR gene alterations and TMB levels in other gynecologic malignancies remain limited [9]. In particular, the contribution of MMR gene variants to TMB-H status in cervical squamous cell carcinoma (CSCC), endometrial endometrioid carcinoma (EEC), and vulvar/vaginal squamous cell carcinoma (V/VSCC) has not been fully elucidated. Therefore, this study aimed to investigate the clinical and molecular features associated with TMB and MMR gene variants in a large, nationally representative Japanese cohort. Using CGP data from a nationwide genomic database, we specifically evaluated the differential contribution of MMR gene alterations to TMB-H status across multiple gynecologic cancer subtypes. The goal of this investigation was to elucidate the genomic determinants of hypermutation and to clarify the potential utility of MMR-related biomarkers in guiding immunotherapy for gynecologic malignancies. Materials and Methods Data Source and Ethical Approval CGP data were obtained from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT), a nationwide cancer genome information management center in Japan. The C-CAT database encompasses CGP data for approximately 99.7% of all patients who underwent CGP testing across Japan [2, 10]. This study was approved by the Ethics Committee of Yamagata University (approval number: 2022-67) and the Ethics Board of C-CAT (approval number: AP20221011-01N). All the data used in this study were anonymized. Patient Selection and Data Extraction To ensure consistency across CGP tests, only data derived from the FoundationOne® CDx platform were included in the analysis. Variables collected included patient age, microsatellite instability (MSI) status, tumor mutational burden (TMB) value, and TMB classification, stratified by cancer type. TMB-high (TMB-H) was defined as a TMB value of ≥10 mutations per megabase (Muts/Mb), consistent with prior studies. Histological classification of each tumor was based on the World Health Organization (WHO) Classification of Tumours, 4 th Edition, 2014 [11], and was registered in the C-CAT system by the treating physician. Due to limitations in the registry, vaginal and vulvar squamous cell carcinomas were analyzed as a single category, as they were not separately classified in the database. Study Cohort The final cohort consisted of 466 cases of CSCC, 317 cases of endometrioid endometrial carcinoma, and 32 cases of vaginal or vulvar squamous cell carcinoma. For each case, data on clinical characteristics, MSI status, TMB values, and somatic as well as germline mutations in mismatch repair (MMR) genes were collected. Genetic Analysis and Variant Classification Mutations in key MMR genes— MLH1 , MSH2 , MSH3 , MSH6 , and PMS2 —were identified and analyzed. Although MSH3 is not traditionally classified as an MMR gene in the Clinical Guidelines for Hereditary Colorectal Cancer [12], we included MSH3 in our analysis based on prior reports demonstrating that the MSH2–MSH3 heterodimer (MutSβ) plays a critical role in the repair of insertion–deletion loops containing two to four extra nucleotides in heteroduplex DNA [13]. The pathogenicity of each variant was determined based on annotations in the ClinVar database [14]. In this study, the category “pathogenic” included variants classified as “likely pathogenic” and those with “conflicting interpretations of pathogenicity” that involved likely pathogenic annotations. Mutation frequency and mutation type were summarized and compared across TMB-H and non–TMB-H groups. Statistical Analysis All statistical analyses were performed using EZR software version 1.37, a graphical interface for R. Fisher’s exact test was employed to assess associations between the presence of specific gene mutations and TMB status (TMB-H vs. non-TMB-H). Both univariate and multivariate logistic regression analyses were performed to evaluate the relationship between MMR gene mutations and TMB-H. A P < 0.05 was considered statistically significant. Use of Artificial Intelligence (AI) Tools During the preparation of this manuscript, an artificial intelligence (AI)–assisted language editing tool was used for partial English language refinement. The authors critically reviewed and edited the AI-assisted output and accept full responsibility for the accuracy and integrity of the manuscript. Results A total of 815 patients (466 CSCC, 317 endometrioid endometrial carcinoma [EEC], and 32 vulvar/vaginal squamous cell carcinoma [V/VSCC]) were included in the analysis. The median age was highest in the vulvar/vaginal carcinoma group, followed by endometrial and cervical cancer (Table 1). MSI-high status was most frequently observed in EEC (16.7%), whereas cervical and vulvar/vaginal cancers showed low frequencies of MSI-high (1.7% and 0%, respectively). The median TMB was also highest in EEC (10.98), compared with cervical (6.8) and vulvar/vaginal carcinoma (5.96). The proportions of TMB-high tumors (≥10 muts/Mb) were 21.7%, 18.6%, and 21.9% in cervical, endometrial, and vulvar/vaginal carcinomas, respectively. Across all tumor types, the number of somatic mutations correlated positively with TMB values (Figure 1). The TMB-high group exhibited significantly greater numbers of somatic mutations than the non–TMB-high group (all p < 0.01). Tumors with higher TMB values also tended to harbor a greater number of MMR gene variants (Figure 2). In the EEC cohort, 81.4% of TMB-high tumors were also classified as MSI-high, demonstrating a strong overlap between TMB-high and MSI-high status. Although a subset of TMB-high tumors occurred in the absence of MSI-high status, the number of such cases was limited, precluding robust subgroup analyses restricted to non–MSI-high tumors. These findings indicate that, in this dataset, TMB-high status largely overlapped with MSI-high status. We next examined the distribution of MMR gene variants according to TMB status. In EEC, all five MMR genes (MLH1, MSH2, MSH3, MSH6, and PMS2) showed significantly higher SNV frequencies in the TMB-high group than in the non–TMB-high group (Table 3). The largest differences were observed for MSH3 (44.1% vs. 6.2%, p < 0.001) and MSH6 (33.9% vs. 6.6%, p < 0.001), demonstrating a strong statistical association with TMB-high status. In CSCC, 101 of 466 cases were classified as TMB-high. A significantly higher frequency of MSH6 SNVs was observed in the TMB-high group compared with the non–TMB-high group (12.9% vs. 4.9%, p = 0.011), whereas differences in other MMR genes did not reach statistical significance (Table 2). In contrast, V/VSCC showed no statistically significant differences in MMR gene variant frequencies between TMB-high and non–TMB-high cases, although the small sample size (n = 32) limited statistical power (Table 4). Multivariate logistic regression analysis in EEC demonstrated that variants in MSH3 (OR: 10.5, 95% CI: 4.69–23.7, p < 0.01) and MSH2 (OR: 9.69, 95% CI: 2.77–34, p < 0.01) were most strongly associated with TMB-high status (Table 5). MLH1 and MSH6 variants were also significantly associated with TMB-high status. According to ClinVar classification (Table 6), a substantial proportion of MSH2, MSH3, and MSH6 variants were annotated as pathogenic (66.7%, 50.0%, and 48.6%, respectively), whereas most MLH1 and PMS2 variants were classified as variants of uncertain significance or not evaluated. When analyses were restricted to pathogenic variants (Table 7), TMB-high EEC cases showed significantly higher frequencies of pathogenic mutations in MSH2 (15.3% vs. 1.2%, p < 0.001), MSH3 (30.5% vs. 1.2%, p < 0.001), and MSH6 (16.9% vs. 3.1%, p < 0.001). These associations remained significant in multivariate analyses (Table 8), in which MSH3 showed the highest odds ratio (OR: 30.8, 95% CI: 8.38–113, p < 0.01). A combined variant analysis of MSH3 and MSH6 demonstrated that tumors harboring both alterations had higher odds of TMB-high status compared with tumors harboring either alteration alone (Table 9). However, given the substantial overlap between TMB-high and MSI-high status in this cohort, these associations should be interpreted in the context of MSI-associated hypermutation. Discussion This nationwide study comprehensively evaluated the relationship between TMB status, MSI status, and MMR gene variants across major gynecologic malignancies using a large-scale Japanese CGP database. The principal finding is that TMB-high status, particularly in endometrioid endometrial carcinoma (EEC), largely overlapped with MSI-high status. In the EEC cohort, 81.4% of TMB-high tumors were concurrently classified as MSI-high, indicating a strong concordance between these two biomarkers. Given this substantial overlap, the enrichment of MMR gene variants observed in TMB-high tumors should be interpreted primarily in the context of MSI-associated hypermutation. Tumors with MSI-high status are characterized by defective DNA mismatch repair, resulting in accelerated mutation accumulation across the genome [8]. Therefore, the higher frequency of MMR gene variants in TMB-high tumors likely reflects secondary accumulation of mutations in hypermutated MSI-high backgrounds rather than independent driver effects of individual MMR gene alterations. The MSH2–MSH3 heterodimer (MutSβ) is a key component of the mismatch repair system and is involved in the recognition and repair of insertion–deletion loops containing two to four unpaired bases [12,13]. Previous studies have reported MSH3 alterations in endometrial carcinoma and have linked them to MSI phenotypes [15]. In addition, germline biallelic MSH3 variants have been associated with hereditary colorectal polyposis, suggesting a role in genomic instability [16]. However, more recent analyses have suggested that MSH3 alterations may also be observed in tumors without classical dMMR features [17]. Differences in patient populations, disease stage, and analytic methodologies may partly explain these discrepancies. In the present study, MSH3 and MSH6 variants were statistically enriched in TMB-high EEC. Multivariate analyses demonstrated strong associations, particularly for MSH3 . However, caution is warranted in attributing a causal role to these alterations. Because the majority of TMB-high tumors were MSI-high, it remains difficult to determine whether MSH3 variants represent true driver events or passenger mutations arising in the setting of MSI-driven genomic instability. Furthermore, variant allele frequency and allelic status (e.g., biallelic inactivation or loss of heterozygosity) were not systematically evaluated, limiting interpretation of their functional significance. It is also well established that MLH1 loss in sporadic endometrial carcinoma frequently occurs through promoter hypermethylation, resulting in epigenetic silencing [18–20]. Because epigenetic alterations were not assessed in the present study, we could not distinguish between mutational and methylation-mediated mechanisms of MMR deficiency. Thus, some TMB-high/MSI-high tumors in this cohort may have been driven primarily by epigenetic MLH1 inactivation rather than coding mutations in MMR genes. In contrast to EEC, the association between MMR gene variants and TMB-high status was limited in cervical and vulvar/vaginal squamous cell carcinomas. These findings suggest that the mechanisms underlying TMB elevation may differ across tumor types. In squamous cell carcinomas, alternative mutational processes, including viral oncogenesis such as HPV-related mutagenesis, may contribute to TMB variability independent of classical MMR deficiency. Collectively, these results highlight the close biological linkage between MSI and TMB in gynecologic malignancies. In this nationwide cohort, TMB-high status was largely explained by MSI-high status, emphasizing the importance of interpreting MMR gene alterations in conjunction with MSI testing. Rather than functioning as independent determinants of hypermutation, many observed MMR gene variants may reflect the genomic instability characteristic of MSI-driven tumors. Nevertheless, the recurrent detection of MSH3 —particularly in combination with MSH6 —raises the possibility that alterations within the MutSβ complex may influence genomic stability in a subset of tumors. Future studies incorporating detailed allelic analyses, functional validation, methylation profiling, and prospective clinical correlation will be necessary to determine whether specific MMR gene alterations provide incremental predictive value beyond MSI status alone. Conclusion In this nationwide Japanese cohort, TMB-high status in gynecologic malignancies—particularly in EEC—largely overlapped with MSI-high status. Although MMR gene alterations, especially in MSH3 and MSH6, were enriched in TMB-high tumors, this finding appears to be largely driven by MSI-associated hypermutation rather than independent driver effects. These results emphasize the close biological relationship between MSI and TMB and underscore the importance of interpreting MMR gene alterations within the context of MSI status. Declarations Acknowledgments We gratefully acknowledge the Center for Cancer Genomics and Advanced Therapeutics (C-CAT), National Cancer Center, Japan, for providing access to the nationwide comprehensive genomic profiling (CGP) database. The use of C-CAT data was essential for conducting this study and enabled the large-scale genomic analysis across gynecologic malignancies. Conflicts of Interest: None References Zhu Binhua, Gu H, Mao Z, et al (2024) Global burden of gynaecological cancers in 2022 and projections to 2050. J Glob Health 14: 04155. 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Int J Clin Oncol doi: 10.1007/s10147-025-02892-1. Online ahead of print. Umar A, Risinger J, Glaab W, et al (1998) Functional overlap in mismatch repair by human MSH3 and MSH6. Genetics, 148:1637–1646. https://www.ncbi.nlm.nih.gov/clinvar/ Accessed Sep 1, 2025 Risinger J, Umar A, Boyd J, et al (1996) Mutation of MSH3 in endometrial cancer and evidence for its functional role in heteroduplex repair. Nat Genet, 14:102–105. Adam R, Spier I, Zhao B, et al (2016) Exome Sequencing Identifies Biallelic MSH3 Germline Mutations as a Recessive Subtype of Colorectal Adenomatous Polyposis. Am J Hum Genet, 99 : 337–351. Cai Y, Wang J, Zhang Z, et al (2025) Mutation profile and molecular heterogeneity in mismatch repair deficient endometrial carcinoma. Front. Oncol. 15:1596879. Simpkins S, Bocker T, Swisher E, et al (1999) Hum Mol Genet. MLH1 promoter methylation and gene silencing is the primary cause of microsatellite instability in sporadic endometrial cancers. 4 : 661-6. 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Characteristics CSCC EEC V/VSCC N 466 317 32 Median age (range) 53 (29-83) 61 (25-85) 66 (41-86) MSI.Status (%) cannot be determined 18 (3.9) 15 ( 4.7) 1 ( 3.1) Equivocal 3 (0.6) 4 ( 1.3) 0 High 8 (1.7) 53 (16.7) 0 Stable 437 (93.8) 245 (77.3) 31 (96.9) TMB.Value 6.8 (6.7) 10.98 (30.14) 5.96 (4.48) TMB-high (%) 101 (21.7) 59 (18.6) 7 (21.9) Number of somatic mutations 20 (2-72) 22.6 (4-236) 14.9 (6-31) Number of DNA somatic mutations (Substitution / insertion / deletion) 5.7 (0-55) 7.1 (0-234) 3.7 (0-14) Number of DNA somatic mutations (Copy number alteration) 0.1 (0-4) 0.5 (0-19) 1.9 (0-14) Number of DNA somatic mutations (Gene rearrangement / structural atypia) 0 0.04 (0-3) 0.28 (0-5) SNV (%) MLH1 24(5.1) 18 (5.7) 2 (6.3) MSH2 14 (3.0) 18 (5.7) 0 MSH3 37 (7.9%) 42 (13.2) 3 (9.4) MSH6 31 (6.6) 37 (11.7) 3 (9.4) PMS2 11 (2.4) 8 (2.5) 0 CNV (%) MLH1 1 (0.2) 1 (0.3) 0 MSH2 0 2 (0.6) 0 MSH3 0 0 0 MSH6 0 1 (0.3) 0 PMS2 0 0 1 (3.1) Table 1. Clinical and Molecular Characteristics of Gynecologic Carcinomas This table presents the clinical and molecular features of patients with three types of gynecologic carcinoma: cervical squamous cell carcinoma, endometrial endometrioid carcinoma, and vulvar/vaginal squamous cell carcinoma. The total number of cases (N) for each cancer type is specified. The median age and age range are provided, along with microsatellite instability (MSI) status categorized into high, stable, equivocal, and undetermined. Tumor mutational burden (TMB) values are reported as mean values with standard deviations, and the percentage of TMB-high cases is indicated. The table also lists the average number of somatic and germline mutations, including details on different types of DNA and RNA mutations (e.g., substitutions, insertions/deletions, copy number alterations, gene rearrangements, and gene fusions). Furthermore, specific mutations in mismatch repair (MMR) genes ( MLH1 , MSH2 , MSH3 , MSH6 , and PMS2 ) are described in terms of single-nucleotide variants (SNVs) and copy number variations. Table 2. The rate of single nucleotide variant about MMR gene in CSCC TMB not-H TMB-H P value N=365 N=101 MLH1 15 ( 4.1) 9 ( 8.9) 0.072 MSH2 11 ( 3.0) 3 ( 3.0) 1 MSH3 26 ( 7.1) 11 ( 10.9) 0.216 MSH6 18 ( 4.9) 13 ( 12.9) 0.011 PMS2 8 ( 2.2) 3 ( 3.0) 0.711 Table 2. Frequency of MMR Gene SNVs by TMB Status in Cervical Squamous Cell Carcinoma This table presents the frequency of single-nucleotide variants (SNVs) in MMR genes among cervical squamous cell carcinoma cases, comparing the TMB-high and TMB-not-high groups. The data include the number and percentage of cases with variants in MLH1 , MSH2 , MSH3 , MSH6 , and PMS2 , along with statistical comparisons. Table 3. The rate of single nucleotide variant about MMR gene in ECC TMB not-H TMB-H P value N=258 N=59 MLH1 9 ( 3.5) 9 ( 15.3) < 0.01 MSH2 6 ( 2.3) 12 ( 20.3) <0.01 MSH3 16 ( 6.2) 26 ( 44.1) <0.01 MSH6 17 ( 6.6) 20 ( 33.9) <0.01 PMS2 2 ( 0.8) 6 ( 10.2) < 0.01 Table 3. Frequency of MMR Gene SNVs by TMB Status in Endometrial Endometrioid Carcinoma This table shows the rate of SNVs in MMR genes among patients with endometrial endometrioid carcinoma, stratified by TMB status. Variants in MLH1 , MSH2 , MSH3 , MSH6 , and PMS2 are presented along with statistical significance for each gene. Table 4. The rate of single nucleotide variant about MMR gene in V/VSCC TMB not-H TMB-H P value N=25 N=7 MLH1 2 ( 8.0) 0 ( 0.0) 1 MSH2 0 0 NA MSH3 3 ( 12.0) 0 ( 0.0) MSH6 3 ( 12.0) 0 ( 0.0) 1 PMS2 0 0 NA Table 4. Frequency of MMR Gene SNVs by TMB Status in Vulvar and Vaginal Carcinomas This table displays the SNV rate in MMR genes in vulvar and vaginal carcinomas, divided by the TMB-high and TMB-not-high groups. For each gene, the number and percentage of cases with variants are shown. Table 5. Contribution of MMR gene to TMB-high in EEC (multivariate analysis) Odds ratio 95%CI P value MLH1 4.36 (1.24-15.3) 0.02 MSH2 9.69 (2.77-34) <0.01 MSH3 10.5 (4.69-23.7) <0.01 MSH6 4.21 (1.65-10.8) <0.01 Table 5. Multivariate Analysis of MMR Gene Contribution to TMB-High in Endometrial Endometrioid Carcinoma This table summarizes the multivariate analysis of the association between specific MMR gene variants and TMB-high status in endometrial endometrioid carcinoma. Odds ratios, 95% confidence intervals, and p-values are reported for each gene. Table 6. The pathogenicity of each MMR gene variant according to ClinVar MLH1 MSH2 MSH3 MSH6 PMS2 Pathogenic 1 (5.6) 12 (66.7) 21 (50.0) 18 (48.6) 1 (12.5) VUS 0 0 10 (23.8) 7 (18.9) 3 (37.5) NE 17 (94.4) 6 (33.3) 11 (26.2) 12 (32.4) 4 (50.0) Total 18 18 42 37 8 Table 6. ClinVar-Based Pathogenicity Classification of MMR Gene Variants This table categorizes the pathogenicity of MMR gene variants in gynecologic carcinomas based on ClinVar classifications. Variants are classified as pathogenic, variants of uncertain significance (VUS), or not evaluated (NE), with frequencies and percentages provided. Table 7. The rate of MMR gene by TMB status in EEC TMB not-H TMB-H p.value N 258 59 MLH1 Variant 9 ( 3.5) 9 (15.3) < 0.01 Pathogenic 0 ( 0.0) 1 ( 1.7) 0.186 MSH2 Variant 6 ( 2.3) 12 (20.3) <0.01 Pathogenic 3 ( 1.2) 9 (15.3) <0.01 MSH3 Variant 16 ( 6.2) 26 (44.1) <0.01 Pathogenic 3 ( 1.2) 18 (30.5) < 0.01 MSH6 Variant 17 ( 6.6) 20 (33.9) < 0.01 Pathogenic 8 ( 3.1) 10 (16.9) < 0.01 PMS2 Variant 2 ( 0.8) 6 (10.2) < 0.01 Pathogenic 0 ( 0.0) 1 ( 1.7) 0.186 Table 7. Frequency and Pathogenicity of MMR Gene Variants by TMB Status in Endometrial Endometrioid Carcinoma This table compares the frequency and pathogenicity of MMR gene variants between the TMB-high and TMB-not-high groups in endometrial endometrioid carcinoma. Data include both total variant and pathogenic variant frequencies for each gene. Table 8. Contribution of MMR gene pathogenic variant to TMB-high in EEC. Odds ratio 95% CI P value MSH2 12.7 2.99-54.1 < 0.01 MSH3 30.8 8.38-113 < 0.01 MSH6 3.79 1.15-12.5 0.03 Table 8. Contribution of Pathogenic MMR Variants to TMB-High in Endometrial Endometrioid Carcinoma This table shows the contribution of pathogenic variants in MMR genes to TMB-high status in endometrial endometrioid carcinoma. Odds ratios, 95% confidence intervals, and p-values are provided for each gene. Table 9. Contribution of MMR gene variant and combination of MSH3 and MSH6 gene variant to TMB-high (multivariate analysis) Odds ratio 95% CI P value MSH3+6+ 16.9 1.64-174.0 0.018 MSH3+6- 9.72 4.01-23.6 < 0.01 MLH1 4.21 1.18-15 0.027 MSH2 9.38 2.64-33.3 < 0.01 MSH6 3.74 1.26-11.2 0.018 PMS2 2.32 0.28-19.2 0.436 Table 9. Multivariate Analysis of MMR Gene Variant Combinations Contributing to TMB-High This table presents the results of a multivariate analysis evaluating the contribution of MMR gene variants—including combinations such as MSH3 and MSH6—to TMB-high status in gynecologic carcinomas. The analysis includes odds ratios, confidence intervals, and p-values for each gene and gene combination. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major revisions 06 May, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 10 Apr, 2026 First submitted to journal 08 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9354189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622751924,"identity":"c05b3407-5c12-42f3-95e5-9aae76d7f534","order_by":0,"name":"MANABU SEINO","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-7511-5190","institution":"Yamagata University - Iida Campus: Yamagata Daigaku - Iida Campus","correspondingAuthor":true,"prefix":"","firstName":"MANABU","middleName":"","lastName":"SEINO","suffix":""},{"id":622751925,"identity":"06b0d15d-f6f3-49c3-a582-1b639da445c1","order_by":1,"name":"Shiori Sano","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Shiori","middleName":"","lastName":"Sano","suffix":""},{"id":622751926,"identity":"8eab946c-ec41-468b-8996-1cb24464f5b4","order_by":2,"name":"Yuka Tachibana","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yuka","middleName":"","lastName":"Tachibana","suffix":""},{"id":622751927,"identity":"3f00554d-2edb-4951-919c-175d1d1eaaac","order_by":3,"name":"Shota Horikawa","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Shota","middleName":"","lastName":"Horikawa","suffix":""},{"id":622751928,"identity":"cb5853ec-20f9-41a4-808e-dade6e2bb632","order_by":4,"name":"Yasufumi Ito","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yasufumi","middleName":"","lastName":"Ito","suffix":""},{"id":622751929,"identity":"414599c7-891e-4490-be54-de95422d812c","order_by":5,"name":"Takeshi Fukunaga","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Takeshi","middleName":"","lastName":"Fukunaga","suffix":""},{"id":622751930,"identity":"224c4f47-7866-492b-9b3d-3c578bb2ab88","order_by":6,"name":"Yosuke Okui","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yosuke","middleName":"","lastName":"Okui","suffix":""},{"id":622751931,"identity":"342640b2-2ac4-47e3-aa7e-637867633c98","order_by":7,"name":"Jun Matsukawa","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Matsukawa","suffix":""},{"id":622751932,"identity":"29d399fb-e1a6-4de3-96ff-873634036205","order_by":8,"name":"Hirotsugu Sakaki","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Hirotsugu","middleName":"","lastName":"Sakaki","suffix":""},{"id":622751933,"identity":"e3626f4c-8bff-4e7f-ad9f-06c54b7cbd9e","order_by":9,"name":"Norikazu Watanabe","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Norikazu","middleName":"","lastName":"Watanabe","suffix":""},{"id":622751934,"identity":"fb78358e-3b4d-4c06-9289-1c0dd29e8c2d","order_by":10,"name":"Keiko Yamanouchi","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Keiko","middleName":"","lastName":"Yamanouchi","suffix":""},{"id":622751935,"identity":"6433b3d7-1a53-46f6-9d03-15fb9bd381ca","order_by":11,"name":"Tsuyoshi Ohta","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Tsuyoshi","middleName":"","lastName":"Ohta","suffix":""},{"id":622751936,"identity":"dcaed13f-180b-4473-a09b-ede486b2bad2","order_by":12,"name":"Yuki Hoshi","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Hoshi","suffix":""},{"id":622751937,"identity":"82df076b-7c9a-4917-ae64-c1d5e2f9fcf2","order_by":13,"name":"Shuhei Suzuki","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Shuhei","middleName":"","lastName":"Suzuki","suffix":""},{"id":622751938,"identity":"175b9abf-a16f-469a-8c5b-57deb8ed0a49","order_by":14,"name":"Masaaki Kawai","email":"","orcid":"","institution":"Juntendo Daigaku - Hongo Campus: Juntendo Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Masaaki","middleName":"","lastName":"Kawai","suffix":""},{"id":622751939,"identity":"ae4b2fe7-1edd-4ebe-b3c7-10f136a883b8","order_by":15,"name":"Satoru Nagase","email":"","orcid":"","institution":"Yamagata University: Yamagata Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Satoru","middleName":"","lastName":"Nagase","suffix":""}],"badges":[],"createdAt":"2026-04-08 08:44:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9354189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9354189/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107450790,"identity":"92db4b8c-4a45-41f5-b7a4-80da75fe45d7","added_by":"auto","created_at":"2026-04-21 15:15:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120227,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of Somatic Mutations by TMB Status in Gynecologic Carcinomas\u003c/p\u003e\n\u003cp\u003eThe number of somatic mutations is compared between the TMB-high and TMB-not-high groups in cervical squamous cell carcinoma, endometrial endometrioid carcinoma, and vulvar/vaginal squamous cell carcinoma. Across all cancer types, the TMB-high group exhibited significantly more mutations, indicating a strong positive correlation between mutation burden and TMB status.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-9354189/v1/8b546ec356ddc2c8fdaa37fb.png"},{"id":107490110,"identity":"6a985d41-960e-4800-a3c1-a0708a8df690","added_by":"auto","created_at":"2026-04-22 02:50:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62612,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation Between the Number of MMR Gene Variants and TMB in Endometrial Endometrioid Carcinoma\u003c/p\u003e\n\u003cp\u003eThis figure shows the relationship between the number of mismatch repair (MMR) gene variants and TMB values in endometrial endometrioid carcinoma. TMB-high tumors were more likely to harbor multiple MMR gene variants.\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-9354189/v1/d7a9c1ee55b3be9f424b89bf.png"},{"id":109069197,"identity":"0a6f5e8c-bdad-4b8d-b29f-832b8fe7b18b","added_by":"auto","created_at":"2026-05-12 10:21:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":556375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9354189/v1/8b9f8376-d0ed-4442-a929-040c958aff43.pdf"}],"financialInterests":"","formattedTitle":"Association Between Tumor Mutation Burden Status and Mismatch Repair Genes in Gynecologic Malignancies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe incidence of gynecologic malignancies has been increasing globally, presenting a significant challenge in clinical oncology [1]. In particular, the management of advanced-stage gynecologic cancers remains difficult due to limited therapeutic options and poor prognoses. Recently, comprehensive genomic profiling (CGP) has gained traction in clinical practice, particularly among patients with advanced or treatment-refractory malignancies [2]. This approach enables the identification of actionable mutations and facilitates personalized, targeted therapeutic strategies. However, several questions remain regarding the clinical utility and interpretability of CGP in gynecologic malignancies [3].\u003c/p\u003e\n\u003cp\u003eAmong emerging molecular biomarkers, immune checkpoint inhibitors (ICIs) have demonstrated promising efficacy across various tumor types [4], especially in those exhibiting microsatellite instability-high (MSI-H) or tumor mutational burden-high (TMB-H) phenotypes [5]. TMB-H, defined as a high number of somatic mutations per megabase, has been proposed as a predictive marker for ICI responsiveness [6]. While environmental factors such as tobacco exposure and ultraviolet radiation are known contributors to elevated TMB, increasing evidence highlights the critical role of mismatch repair (MMR) gene deficiencies in hypermutated tumor pathogenesis [7].\u003c/p\u003e\n\u003cp\u003eDespite extensive research in colorectal cancer and endometrial cancer, where MMR deficiency is well established as a driver of hypermutation [8], comprehensive analyses of the relationship between MMR gene alterations and TMB levels in other gynecologic malignancies remain limited [9]. In particular, the contribution of MMR gene variants to TMB-H status in cervical squamous cell carcinoma (CSCC), endometrial endometrioid carcinoma (EEC), and vulvar/vaginal squamous cell carcinoma (V/VSCC) has not been fully elucidated.\u003c/p\u003e\n\u003cp\u003eTherefore, this study aimed to investigate the clinical and molecular features associated with TMB and MMR gene variants in a large, nationally representative Japanese cohort. Using CGP data from a nationwide genomic database, we specifically evaluated the differential contribution of MMR gene alterations to TMB-H status across multiple gynecologic cancer subtypes. The goal of this investigation was to elucidate the genomic determinants of hypermutation and to clarify the potential utility of MMR-related biomarkers in guiding immunotherapy for gynecologic malignancies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData Source and Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCGP data were obtained from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT), a nationwide cancer genome information management center in Japan. The C-CAT database encompasses CGP data for approximately 99.7% of all patients who underwent CGP testing across Japan [2, 10]. This study was approved by the Ethics Committee of Yamagata University (approval number: 2022-67) and the Ethics Board of C-CAT (approval number: AP20221011-01N). All the data used in this study were anonymized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Selection and Data Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure consistency across CGP tests, only data derived from the FoundationOne® CDx platform were included in the analysis. Variables collected included patient age, microsatellite instability (MSI) status, tumor mutational burden (TMB) value, and TMB classification, stratified by cancer type. TMB-high (TMB-H) was defined as a TMB value of ≥10 mutations per megabase (Muts/Mb), consistent with prior studies.\u003c/p\u003e\n\u003cp\u003eHistological classification of each tumor was based on the World Health Organization (WHO) Classification of Tumours, 4\u003csup\u003eth\u003c/sup\u003e Edition, 2014 [11], and was registered in the C-CAT system by the treating physician. Due to limitations in the registry, vaginal and vulvar squamous cell carcinomas were analyzed as a single category, as they were not separately classified in the database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final cohort consisted of 466 cases of CSCC, 317 cases of endometrioid endometrial carcinoma, and 32 cases of vaginal or vulvar squamous cell carcinoma.\u003c/p\u003e\n\u003cp\u003eFor each case, data on clinical characteristics, MSI status, TMB values, and somatic as well as germline mutations in mismatch repair (MMR) genes were collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic Analysis and Variant Classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMutations in key MMR genes—\u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, \u003cem\u003eMSH3\u003c/em\u003e, \u003cem\u003eMSH6\u003c/em\u003e, and \u003cem\u003ePMS2\u003c/em\u003e—were identified and analyzed. Although \u003cem\u003eMSH3\u003c/em\u003e is not traditionally classified as an MMR gene in the Clinical Guidelines for Hereditary Colorectal Cancer [12], we included \u003cem\u003eMSH3\u003c/em\u003e in our analysis based on prior reports demonstrating that the MSH2–MSH3 heterodimer (MutSβ) plays a critical role in the repair of insertion–deletion loops containing two to four extra nucleotides in heteroduplex DNA [13]. The pathogenicity of each variant was determined based on annotations in the ClinVar database [14]. In this study, the category “pathogenic” included variants classified as “likely pathogenic” and those with “conflicting interpretations of pathogenicity” that involved likely pathogenic annotations. Mutation frequency and mutation type were summarized and compared across TMB-H and non–TMB-H groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using EZR software version 1.37, a graphical interface for R. Fisher’s exact test was employed to assess associations between the presence of specific gene mutations and TMB status (TMB-H vs. non-TMB-H). Both univariate and multivariate logistic regression analyses were performed to evaluate the relationship between MMR gene mutations and TMB-H. A P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of Artificial Intelligence (AI) Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this manuscript, an artificial intelligence (AI)–assisted language editing tool was used for partial English language refinement. The authors critically reviewed and edited the AI-assisted output and accept full responsibility for the accuracy and integrity of the manuscript.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 815 patients (466 CSCC, 317 endometrioid endometrial carcinoma [EEC], and 32 vulvar/vaginal squamous cell carcinoma [V/VSCC]) were included in the analysis. The median age was highest in the vulvar/vaginal carcinoma group, followed by endometrial and cervical cancer (Table 1). MSI-high status was most frequently observed in EEC (16.7%), whereas cervical and vulvar/vaginal cancers showed low frequencies of MSI-high (1.7% and 0%, respectively). The median TMB was also highest in EEC (10.98), compared with cervical (6.8) and vulvar/vaginal carcinoma (5.96). The proportions of TMB-high tumors (≥10 muts/Mb) were 21.7%, 18.6%, and 21.9% in cervical, endometrial, and vulvar/vaginal carcinomas, respectively.\u003c/p\u003e\n\u003cp\u003eAcross all tumor types, the number of somatic mutations correlated positively with TMB values (Figure 1). The TMB-high group exhibited significantly greater numbers of somatic mutations than the non–TMB-high group (all p \u0026lt; 0.01). Tumors with higher TMB values also tended to harbor a greater number of MMR gene variants (Figure 2).\u003c/p\u003e\n\u003cp\u003eIn the EEC cohort, 81.4% of TMB-high tumors were also classified as MSI-high, demonstrating a strong overlap between TMB-high and MSI-high status. Although a subset of TMB-high tumors occurred in the absence of MSI-high status, the number of such cases was limited, precluding robust subgroup analyses restricted to non–MSI-high tumors. These findings indicate that, in this dataset, TMB-high status largely overlapped with MSI-high status.\u003c/p\u003e\n\u003cp\u003eWe next examined the distribution of MMR gene variants according to TMB status. In EEC, all five MMR genes (MLH1, MSH2, MSH3, MSH6, and PMS2) showed significantly higher SNV frequencies in the TMB-high group than in the non–TMB-high group (Table 3). The largest differences were observed for MSH3 (44.1% vs. 6.2%, p \u0026lt; 0.001) and MSH6 (33.9% vs. 6.6%, p \u0026lt; 0.001), demonstrating a strong statistical association with TMB-high status.\u003c/p\u003e\n\u003cp\u003eIn CSCC, 101 of 466 cases were classified as TMB-high. A significantly higher frequency of MSH6 SNVs was observed in the TMB-high group compared with the non–TMB-high group (12.9% vs. 4.9%, p = 0.011), whereas differences in other MMR genes did not reach statistical significance (Table 2). In contrast, V/VSCC showed no statistically significant differences in MMR gene variant frequencies between TMB-high and non–TMB-high cases, although the small sample size (n = 32) limited statistical power (Table 4).\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression analysis in EEC demonstrated that variants in MSH3 (OR: 10.5, 95% CI: 4.69–23.7, p \u0026lt; 0.01) and MSH2 (OR: 9.69, 95% CI: 2.77–34, p \u0026lt; 0.01) were most strongly associated with TMB-high status (Table 5). MLH1 and MSH6 variants were also significantly associated with TMB-high status.\u003c/p\u003e\n\u003cp\u003eAccording to ClinVar classification (Table 6), a substantial proportion of MSH2, MSH3, and MSH6 variants were annotated as pathogenic (66.7%, 50.0%, and 48.6%, respectively), whereas most MLH1 and PMS2 variants were classified as variants of uncertain significance or not evaluated.\u003c/p\u003e\n\u003cp\u003eWhen analyses were restricted to pathogenic variants (Table 7), TMB-high EEC cases showed significantly higher frequencies of pathogenic mutations in MSH2 (15.3% vs. 1.2%, p \u0026lt; 0.001), MSH3 (30.5% vs. 1.2%, p \u0026lt; 0.001), and MSH6 (16.9% vs. 3.1%, p \u0026lt; 0.001). These associations remained significant in multivariate analyses (Table 8), in which MSH3 showed the highest odds ratio (OR: 30.8, 95% CI: 8.38–113, p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003eA combined variant analysis of MSH3 and MSH6 demonstrated that tumors harboring both alterations had higher odds of TMB-high status compared with tumors harboring either alteration alone (Table 9). However, given the substantial overlap between TMB-high and MSI-high status in this cohort, these associations should be interpreted in the context of MSI-associated hypermutation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis nationwide study comprehensively evaluated the relationship between TMB status, MSI status, and MMR gene variants across major gynecologic malignancies using a large-scale Japanese CGP database. The principal finding is that TMB-high status, particularly in endometrioid endometrial carcinoma (EEC), largely overlapped with MSI-high status. In the EEC cohort, 81.4% of TMB-high tumors were concurrently classified as MSI-high, indicating a strong concordance between these two biomarkers.\u003c/p\u003e\n\u003cp\u003eGiven this substantial overlap, the enrichment of MMR gene variants observed in TMB-high tumors should be interpreted primarily in the context of MSI-associated hypermutation. Tumors with MSI-high status are characterized by defective DNA mismatch repair, resulting in accelerated mutation accumulation across the genome [8]. Therefore, the higher frequency of MMR gene variants in TMB-high tumors likely reflects secondary accumulation of mutations in hypermutated MSI-high backgrounds rather than independent driver effects of individual MMR gene alterations.\u003c/p\u003e\n\u003cp\u003eThe MSH2–MSH3 heterodimer (MutSβ) is a key component of the mismatch repair system and is involved in the recognition and repair of insertion–deletion loops containing two to four unpaired bases [12,13]. Previous studies have reported \u003cem\u003eMSH3\u003c/em\u003e alterations in endometrial carcinoma and have linked them to MSI phenotypes [15]. In addition, germline biallelic \u003cem\u003eMSH3\u003c/em\u003e variants have been associated with hereditary colorectal polyposis, suggesting a role in genomic instability [16]. However, more recent analyses have suggested that \u003cem\u003eMSH3\u003c/em\u003e alterations may also be observed in tumors without classical dMMR features [17]. Differences in patient populations, disease stage, and analytic methodologies may partly explain these discrepancies.\u003c/p\u003e\n\u003cp\u003eIn the present study, \u003cem\u003eMSH3\u003c/em\u003e and \u003cem\u003eMSH6\u003c/em\u003e variants were statistically enriched in TMB-high EEC. Multivariate analyses demonstrated strong associations, particularly for \u003cem\u003eMSH3\u003c/em\u003e. However, caution is warranted in attributing a causal role to these alterations. Because the majority of TMB-high tumors were MSI-high, it remains difficult to determine whether \u003cem\u003eMSH3\u003c/em\u003e variants represent true driver events or passenger mutations arising in the setting of MSI-driven genomic instability. Furthermore, variant allele frequency and allelic status (e.g., biallelic inactivation or loss of heterozygosity) were not systematically evaluated, limiting interpretation of their functional significance.\u003c/p\u003e\n\u003cp\u003eIt is also well established that \u003cem\u003eMLH1\u003c/em\u003e loss in sporadic endometrial carcinoma frequently occurs through promoter hypermethylation, resulting in epigenetic silencing [18–20]. Because epigenetic alterations were not assessed in the present study, we could not distinguish between mutational and methylation-mediated mechanisms of MMR deficiency. Thus, some TMB-high/MSI-high tumors in this cohort may have been driven primarily by epigenetic \u003cem\u003eMLH1\u003c/em\u003e inactivation rather than coding mutations in MMR genes.\u003c/p\u003e\n\u003cp\u003eIn contrast to EEC, the association between MMR gene variants and TMB-high status was limited in cervical and vulvar/vaginal squamous cell carcinomas. These findings suggest that the mechanisms underlying TMB elevation may differ across tumor types. In squamous cell carcinomas, alternative mutational processes, including viral oncogenesis such as HPV-related mutagenesis, may contribute to TMB variability independent of classical MMR deficiency.\u003c/p\u003e\n\u003cp\u003eCollectively, these results highlight the close biological linkage between MSI and TMB in gynecologic malignancies. In this nationwide cohort, TMB-high status was largely explained by MSI-high status, emphasizing the importance of interpreting MMR gene alterations in conjunction with MSI testing. Rather than functioning as independent determinants of hypermutation, many observed MMR gene variants may reflect the genomic instability characteristic of MSI-driven tumors.\u003c/p\u003e\n\u003cp\u003eNevertheless, the recurrent detection of \u003cem\u003eMSH3\u003c/em\u003e—particularly in combination with \u003cem\u003eMSH6\u003c/em\u003e—raises the possibility that alterations within the MutSβ complex may influence genomic stability in a subset of tumors. Future studies incorporating detailed allelic analyses, functional validation, methylation profiling, and prospective clinical correlation will be necessary to determine whether specific MMR gene alterations provide incremental predictive value beyond MSI status alone.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this nationwide Japanese cohort, TMB-high status in gynecologic malignancies—particularly in EEC—largely overlapped with MSI-high status. Although MMR gene alterations, especially in MSH3 and MSH6, were enriched in TMB-high tumors, this finding appears to be largely driven by MSI-associated hypermutation rather than independent driver effects. These results emphasize the close biological relationship between MSI and TMB and underscore the importance of interpreting MMR gene alterations within the context of MSI status.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the Center for Cancer Genomics and Advanced Therapeutics (C-CAT), National Cancer Center, Japan, for providing access to the nationwide comprehensive genomic profiling (CGP) database. The use of C-CAT data was essential for conducting this study and enabled the large-scale genomic analysis across gynecologic malignancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhu Binhua, Gu H, Mao Z, et al (2024) Global burden of gynaecological cancers in 2022 and projections to 2050. J Glob Health 14: 04155.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMukai Y, Ueno H (2021) Establishment and implementation of Cancer Genomic Medicine in Japan. Cancer Sci, 112: 970-977.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVolders P, Aftimos P, Dedeurwaerdere F, et al (2025) A nationwide comprehensive genomic profiling and molecular tumor board platform for patients with advanced cancer. NPJ Precis Oncol. 10 ; 9:66.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLi B, Chan H, Chen P (2019) Immune Checkpoint Inhibitors: Basics and Challenges. Curr Med Chem. 26(17):3009-3025,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eO\u0026apos;Malley D, Bariani G, Cassier A, et al (2022) Pembrolizumab in Patients With Microsatellite Instability-High Advanced Endometrial Cancer: Results From the KEYNOTE-158 Study. JCO, 40, 752-761,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMarabelle A, Fakih M, Lopez J, et al (2020) Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol, 21, 1353-65,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSung J, Park D, Lee S (2022) High Tumor Mutation Burden Is Associated with Poor Clinical Outcome in EGFR-Mutated Lung Adenocarcinomas Treated with Targeted Therapy. Biomedicines. 10(9) : 2109.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCancer Genome Atlas Research Network; Kandoth C, Schultz N, Cherniack A, et al (2013) Integrated genomic characterization of endometrial carcinoma, Nature. 497 : 67\u0026ndash;73.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eXi Q, Kage H, Ogawa M, et al (2023) Genomic Landscape of Endometrial, Ovarian, and Cervical Cancers in Japan from the Database in the Center for Cancer Genomics and Advanced Therapeutics. Cancers (Basel). 16(1) : 136.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ehttps://for-patients.c-cat.ncc.go.jp/registration_status/ Accessed Sep 1, 2025\u003c/li\u003e\n \u003cli\u003eWHO Classification of Tumours of Female Reproductive Organs. WHO Classification of Tumours, 4th Edition, 2014.\u003c/li\u003e\n \u003cli\u003eTanakaya K, Yamaguchi T, Hirata K, et al (2025) Japanese society for cancer of the colon and rectum (JSCCR) guidelines 2024 for the clinical practice of hereditary colorectal cancer. Int J Clin Oncol doi: 10.1007/s10147-025-02892-1. Online ahead of print.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eUmar A, Risinger J, Glaab W, et al (1998) Functional overlap in mismatch repair by human MSH3 and MSH6. Genetics, 148:1637\u0026ndash;1646.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ehttps://www.ncbi.nlm.nih.gov/clinvar/\u0026nbsp;Accessed Sep 1, 2025\u003c/li\u003e\n \u003cli\u003eRisinger J, Umar A, Boyd J, et al (1996) Mutation of MSH3 in endometrial cancer and evidence for its functional role in heteroduplex repair. Nat Genet, 14:102\u0026ndash;105.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAdam R, Spier I, Zhao B, et al (2016) Exome Sequencing Identifies Biallelic MSH3 Germline Mutations as a Recessive Subtype of Colorectal Adenomatous Polyposis. Am J Hum Genet, 99 : 337\u0026ndash;351.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCai Y, Wang J, Zhang Z, et al (2025) Mutation profile and molecular heterogeneity in mismatch repair deficient endometrial carcinoma. Front. Oncol. 15:1596879.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSimpkins S, Bocker T, Swisher E, et al (1999) Hum Mol Genet. MLH1 promoter methylation and gene silencing is the primary cause of microsatellite instability in sporadic endometrial cancers. 4 : 661-6.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eXiong Y, Dowdy S, Eberhardt N, et al (2006) hMLH1 promoter methylation and silencing in primary endometrial cancers are associated with specific alterations in MBDs occupancy and histone modifications. Gynecol Oncol. 103(1) : 321\u0026ndash;328.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWard R, Dobbins T, Lindor N, et al (2013) Identification of constitutional MLH1 epimutations and promoter variants in colorectal cancer patients from the Colon Cancer Family Registry. Genetics in Medicine volume. 15 : 25\u0026ndash;35.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eEEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eV/VSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eMedian age (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e53 (29-83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e61 (25-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e66 (41-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eMSI.Status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003ecannot be determined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e18 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;15 ( 4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;1 ( 3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eEquivocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4 ( 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;53 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eStable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e437 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e245 (77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e31 (96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eTMB.Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6.8 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e10.98 (30.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e5.96 (4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eTMB-high (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e101 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;59 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;7 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eNumber of somatic mutations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e20 (2-72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e22.6 (4-236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e14.9 (6-31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eNumber of DNA somatic mutations (Substitution / insertion / deletion)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5.7 (0-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e7.1 (0-234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e3.7 (0-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eNumber of DNA somatic mutations (Copy number alteration)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.1 (0-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.5 (0-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.9 (0-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eNumber of DNA somatic mutations (Gene rearrangement / structural atypia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.04 (0-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.28 (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eSNV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e24(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e18 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e14 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e18 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e37 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e42 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e3 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e31 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e37 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e3 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e8 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eCNV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Clinical and Molecular Characteristics of Gynecologic Carcinomas\u003c/p\u003e\n\u003cp\u003eThis table presents the clinical and molecular features of patients with three types of gynecologic carcinoma: cervical squamous cell carcinoma, endometrial endometrioid carcinoma, and vulvar/vaginal squamous cell carcinoma. The total number of cases (N) for each cancer type is specified. The median age and age range are provided, along with microsatellite instability (MSI) status categorized into high, stable, equivocal, and undetermined. Tumor mutational burden (TMB) values are reported as mean values with standard deviations, and the percentage of TMB-high cases is indicated. The table also lists the average number of somatic and germline mutations, including details on different types of DNA and RNA mutations (e.g., substitutions, insertions/deletions, copy number alterations, gene rearrangements, and gene fusions). Furthermore, specific mutations in mismatch repair (MMR) genes (\u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, \u003cem\u003eMSH3\u003c/em\u003e, \u003cem\u003eMSH6\u003c/em\u003e, and \u003cem\u003ePMS2\u003c/em\u003e) are described in terms of single-nucleotide variants (SNVs) and copy number variations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. The rate of single nucleotide variant about MMR gene in CSCC\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eTMB not-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eTMB-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eN=365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eN=101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e15 ( 4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e9 ( 8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e11 ( 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3 ( 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e26 ( 7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e11 ( 10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e18 ( 4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e13 ( 12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e8 ( 2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3 ( 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Frequency of MMR Gene SNVs by TMB Status in Cervical Squamous Cell Carcinoma\u003c/p\u003e\n\u003cp\u003eThis table presents the frequency of single-nucleotide variants (SNVs) in MMR genes among cervical squamous cell carcinoma cases, comparing the TMB-high and TMB-not-high groups. The data include the number and percentage of cases with variants in \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, \u003cem\u003eMSH3\u003c/em\u003e, \u003cem\u003eMSH6\u003c/em\u003e, and \u003cem\u003ePMS2\u003c/em\u003e, along with statistical comparisons.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. The rate of single nucleotide variant about MMR gene in ECC\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eTMB not-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eTMB-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eN=258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eN=59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp; 9 ( \u0026nbsp;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;9 ( 15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp; 6 ( \u0026nbsp;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e12 ( 20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;16 ( \u0026nbsp;6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e26 ( 44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;17 ( \u0026nbsp;6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e20 ( 33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2 ( \u0026nbsp;0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;6 ( 10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Frequency of MMR Gene SNVs by TMB Status in Endometrial Endometrioid Carcinoma\u003c/p\u003e\n\u003cp\u003eThis table shows the rate of SNVs in MMR genes among patients with endometrial endometrioid carcinoma, stratified by TMB status. Variants in \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, \u003cem\u003eMSH3\u003c/em\u003e, \u003cem\u003eMSH6\u003c/em\u003e, and \u003cem\u003ePMS2\u003c/em\u003e are presented along with statistical significance for each gene.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. The rate of single nucleotide variant about MMR gene in V/VSCC\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eTMB not-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eTMB-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eN=25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eN=7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;2 ( \u0026nbsp;8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0 ( \u0026nbsp;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;NA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;3 ( 12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0 ( \u0026nbsp;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;3 ( 12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0 ( \u0026nbsp;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;NA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Frequency of MMR Gene SNVs by TMB Status in Vulvar and Vaginal Carcinomas\u003c/p\u003e\n\u003cp\u003eThis table displays the SNV rate in MMR genes in vulvar and vaginal carcinomas, divided by the TMB-high and TMB-not-high groups. For each gene, the number and percentage of cases with variants are shown.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Contribution of MMR gene to TMB-high in EEC (multivariate analysis)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 107px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e(1.24-15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e9.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e(2.77-34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e(4.69-23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e(1.65-10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5. Multivariate Analysis of MMR Gene Contribution to TMB-High in Endometrial Endometrioid Carcinoma\u003c/p\u003e\n\u003cp\u003eThis table summarizes the multivariate analysis of the association between specific MMR gene variants and TMB-high status in endometrial endometrioid carcinoma. Odds ratios, 95% confidence intervals, and p-values are reported for each gene.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. The pathogenicity of each MMR gene variant according to ClinVar\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e12 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e21 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e18 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eVUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e10 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e7 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e3 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e17 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e6 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e11 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e12 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6. ClinVar-Based Pathogenicity Classification of MMR Gene Variants\u003c/p\u003e\n\u003cp\u003eThis table categorizes the pathogenicity of MMR gene variants in gynecologic carcinomas based on ClinVar classifications. Variants are classified as pathogenic, variants of uncertain significance (VUS), or not evaluated (NE), with frequencies and percentages provided.\u003c/p\u003e\n\u003cp\u003eTable 7. The rate of MMR gene by TMB status in EEC\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eTMB not-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eTMB-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ep.value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eVariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp; 9 ( \u0026nbsp;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;9 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp; 0 ( \u0026nbsp;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;1 ( 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eVariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp; 6 ( \u0026nbsp;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e12 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp; 3 ( \u0026nbsp;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;9 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eVariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;16 ( \u0026nbsp;6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e26 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp; 3 ( \u0026nbsp;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e18 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eVariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;17 ( \u0026nbsp;6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e20 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp; 8 ( \u0026nbsp;3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e10 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eVariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2 ( \u0026nbsp;0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;6 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp; 0 ( \u0026nbsp;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;1 ( 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7. Frequency and Pathogenicity of MMR Gene Variants by TMB Status in Endometrial Endometrioid Carcinoma\u003c/p\u003e\n\u003cp\u003eThis table compares the frequency and pathogenicity of MMR gene variants between the TMB-high and TMB-not-high groups in endometrial endometrioid carcinoma. Data include both total variant and pathogenic variant frequencies for each gene.\u003c/p\u003e\n\u003cp\u003eTable 8. Contribution of MMR gene pathogenic variant to TMB-high in EEC.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 386px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2.99-54.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 386px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e8.38-113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 386px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.15-12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 386px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 8. Contribution of Pathogenic MMR Variants to TMB-High in Endometrial Endometrioid Carcinoma\u003c/p\u003e\n\u003cp\u003eThis table shows the contribution of pathogenic variants in MMR genes to TMB-high status in endometrial endometrioid carcinoma. Odds ratios, 95% confidence intervals, and p-values are provided for each gene.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 9. Contribution of MMR gene variant and combination of \u003cem\u003eMSH3\u003c/em\u003e and \u003cem\u003eMSH6\u003c/em\u003e gene variant to TMB-high (multivariate analysis)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"764\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 377px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3+6+ \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.64-174.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 377px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH3+6- \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e9.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4.01-23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 377px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 377px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.18-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 377px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2.64-33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 377px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.26-11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 377px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.28-19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 377px;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 9. Multivariate Analysis of MMR Gene Variant Combinations Contributing to TMB-High\u003c/p\u003e\n\u003cp\u003eThis table presents the results of a multivariate analysis evaluating the contribution of MMR gene variants\u0026mdash;including combinations such as MSH3 and MSH6\u0026mdash;to TMB-high status in gynecologic carcinomas. The analysis includes odds ratios, confidence intervals, and p-values for each gene and gene combination.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9354189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9354189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThis study investigated the relationship between tumor mutation burden (TMB) status and mismatch repair (MMR) gene variants in gynecologic malignancies using cancer gene panel testing data in Japan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe analyzed data from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) for cases tested between March 2018 and June 2023. A total of 317 cases of endometrioid endometrial carcinoma (EEC), 669 cases of cervical squamous cell carcinoma (CSCC), and 32 cases of vaginal or vulvar squamous cell carcinoma (V/VSCC) were included. TMB-high was defined as ≥10 mutations per megabase (Muts/Mb). The detection rates of MMR gene variants (\u003cem\u003eMSH2, MSH3, MSH6, MLH1\u003c/em\u003e, and \u003cem\u003ePMS2\u003c/em\u003e) were compared between TMB-high and TMB-not-high groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn the EEC cohort, approximately 80% of TMB-high tumors were also classified as MSI-high, demonstrating a strong overlap between these biomarkers. TMB-high tumors showed increased frequencies of MMR gene variants, particularly in \u003cem\u003eMSH3\u003c/em\u003e and \u003cem\u003eMSH6\u003c/em\u003e. However, given the substantial concordance between TMB-high and MSI-high status, this enrichment likely reflects mutation accumulation driven by MSI-associated hypermutation rather than independent oncogenic effects of specific MMR gene alterations. In contrast, in CSCC and V/VSCC, the association between MMR gene variants and TMB-high status was limited and less pronounced.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn this nationwide cohort, TMB-high status in gynecologic malignancies—especially in EEC—largely overlaps with MSI-high status. The enrichment of MMR gene variants in TMB-high tumors appears to be strongly influenced by MSI-driven hypermutation, highlighting the importance of interpreting MMR alterations within the context of MSI status.\u003c/p\u003e","manuscriptTitle":"Association Between Tumor Mutation Burden Status and Mismatch Repair Genes in Gynecologic Malignancies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 15:15:17","doi":"10.21203/rs.3.rs-9354189/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2026-05-06T16:25:25+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-04-17T01:30:40+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T06:51:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T04:56:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Clinical Oncology","date":"2026-04-08T04:39:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a097f2a5-2368-46d8-8c15-2cd18a7c7576","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Major revisions","date":"2026-05-06T16:25:25+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T20:25:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 15:15:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9354189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9354189","identity":"rs-9354189","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}