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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Pathol. Oncol. Res.</journal-id>
<journal-title-group>
<journal-title>Pathology &#x26; Oncology Research</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Pathol. Oncol. Res.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1532-2807</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">1612465</article-id>
<article-id pub-id-type="doi">10.3389/pore.2026.1612465</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Molecular classification of endometrial carcinoma: clinical utility of an NGS panel with targeted detection of 116 cancer-related genes</article-title>
<alt-title alt-title-type="left-running-head">Chen et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/pore.2026.1612465">10.3389/pore.2026.1612465</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Chen</surname>
<given-names>Lingfeng</given-names>
</name>
<xref ref-type="aff" rid="aff1"/>
<xref ref-type="author-notes" rid="fn001">
<sup>&#x2020;</sup>
</xref>
</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>You</surname>
<given-names>Zhijie</given-names>
</name>
<xref ref-type="aff" rid="aff1"/>
<xref ref-type="author-notes" rid="fn001">
<sup>&#x2020;</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yu</surname>
<given-names>Xunbin</given-names>
</name>
<xref ref-type="aff" rid="aff1"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wu</surname>
<given-names>Yijuan</given-names>
</name>
<xref ref-type="aff" rid="aff1"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Xin</given-names>
</name>
<xref ref-type="aff" rid="aff1"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Lin</surname>
<given-names>Jie</given-names>
</name>
<xref ref-type="aff" rid="aff1"/>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/3466642"/>
</contrib>
</contrib-group>
<aff id="aff1">
<institution>Department of Pathology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital</institution>, <city>Fuzhou</city>, <state>Fujian</state>, <country country="CN">China</country>
</aff>
<author-notes>
<corresp id="c001">
<label>&#x2a;</label>Correspondence: Jie Lin, <email xlink:href="mailto:sllinjie@163.com">sllinjie@163.com</email>
</corresp>
<fn fn-type="equal" id="fn001">
<label>&#x2020;</label>
<p>These authors have contributed equally to this work</p>
</fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-07-28">
<day>28</day>
<month>07</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>32</volume>
<elocation-id>1612465</elocation-id>
<history>
<date date-type="received">
<day>30</day>
<month>04</month>
<year>2026</year>
</date>
<date date-type="rev-recd">
<day>10</day>
<month>07</month>
<year>2026</year>
</date>
<date date-type="accepted">
<day>21</day>
<month>07</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Chen, You, Yu, Wu, Chen and Lin.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Chen, You, Yu, Wu, Chen and Lin</copyright-holder>
<license>
<ali:license_ref start_date="2026-07-28">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>This study aimed to evaluate the efficacy of a single-test, targeted DNA next-generation sequencing (NGS) panel in classifying endometrial carcinoma (EC) into molecular subtypes and to compare its performance with that of the established Sanger sequencing &#x2b; immunohistochemistry (Sanger &#x2b; IHC) molecular classification.</p>
</sec>
<sec>
<title>Methods</title>
<p>Targeted DNA NGS was performed on 131 samples using the clinically validated AmoyDx&#xae; Comprehensive Panel, and a commercially available targeted AmoyDx EC Panel covering POLE, TP53, and MSI was used for 63 samples.</p>
</sec>
<sec>
<title>Results</title>
<p>The concordance between the NGS and Sanger &#x2b; IHC classifications was 93.8% (182/194 cases), with a kappa value of 0.908. The exclusion of seven discordant <italic>POLE</italic> mutations improved concordance to 97.4% (kappa &#x3d; 0.962). NGS identified 30 <italic>POLE</italic> mutations compared to 23 detected by Sanger sequencing, which missed low-frequency variants. Microsatellite instability (MSI) analysis and mismatch repair (MMR) immunohistochemistry (IHC) results were highly concordant (97.9%). However, NGS-based <italic>TP53</italic> mutation detection showed moderate agreement with the p53 IHC results (kappa &#x3d; 0.688). Mutations associated with targeted therapy trials, including PTEN (76.3%), PIK3CA (50.4%), and ARID1A (35.9%), were found in 131&#xa0;EC samples.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>These findings indicate that NGS-based molecular classification aligns well with Sanger &#x2b; IHC molecular classification and offers higher sensitivity than Sanger sequencing, thereby improving the identification of mutations associated with targeted therapy trials. This enhances the prognosis and treatment planning for patients with advanced EC.</p>
</sec>
</abstract>
<kwd-group>
<kwd>endometrial carcinoma</kwd>
<kwd>immunohistochemistry</kwd>
<kwd>molecular classification</kwd>
<kwd>NGS</kwd>
<kwd>Sanger sequencing</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the Joint Funds for the Innovation of Science and Technology, Fujian Province, under Grant number 2023Y9312, and the Natural Science Foundation of Fujian Province, under Grant number 2023J011162. It is important to note that funding organizations were not involved in the study design, data collection and analysis, decision to publish, or manuscript preparation.</funding-statement>
</funding-group>
<counts>
<fig-count count="6"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="33"/>
<page-count count="14"/>
</counts>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>Endometrial carcinoma (EC) is a significant health concern, being the second most common malignant tumor of the female reproductive system in China and the leading gynecological cancer in developed countries [<xref ref-type="bibr" rid="B1">1</xref>]. Traditionally, the prognosis and treatment strategies for EC are guided by histopathological factors. However, recent advancements in molecular biology have underscored the importance of molecular classification in refining prognostic assessments and therapeutic decisions.</p>
<p>In 2013, The Cancer Genome Atlas (TCGA) project revolutionized our understanding of EC by introducing a molecular classification system based on comprehensive multi-omics analyses, including whole genome sequencing, whole exome sequencing, methylation profiling, transcriptomic analysis, and proteomics [<xref ref-type="bibr" rid="B2">2</xref>]. The TransPORTEC classification and Proactive Molecular Risk Classifier for EC (ProMisE) have also been developed. The ProMisE categories consist of four distinct molecular subtypes: POLE ultramutated (POLEmut), mismatch repair deficient (MMRd), p53 abnormal (p53abn), and non-specific molecular profile (NSMP) [<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B4">4</xref>]. These classifications have provided deeper insights into the heterogeneity of EC, revealing significant variations in the prognosis and response to treatment among EC subtypes.</p>
<p>Building on the TCGA framework, the 5th edition of the World Health Organization (WHO) classification of female genital tumors in 2020 incorporated molecular classification criteria, enabling direct clinical application through DNA sequencing and immunohistochemistry (IHC) techniques [<xref ref-type="bibr" rid="B5">5</xref>]. This integration into clinical guidelines is further supported by the 2023 staging recommendations from the International Federation of Gynecology and Obstetrics (FIGO), which advocates for comprehensive molecular classification testing for all EC patients [<xref ref-type="bibr" rid="B6">6</xref>]. Despite these advancements, the implementation of molecular classification in clinical practice remains inconsistent, primarily because of the complexity and cost associated with multi-platform testing methods, such as combining Sanger sequencing or next-generation sequencing (NGS) with IHC.</p>
<p>Approximately 3% of EC cases exhibit overlapping molecular characteristics, complicating the accurate determination of their molecular subtypes without comprehensive testing of all relevant molecular markers, including <italic>POLE</italic> mutations, MMR status, and p53 abnormalities [<xref ref-type="bibr" rid="B7">7</xref>]. The NSMP subtype, often referred to as the &#x201c;junk category,&#x201d; encompasses EC cases that do not exhibit <italic>POLE</italic> mutations, MMR deficiencies, or p53 abnormalities. This subtype accounts for a substantial proportion of EC cases, ranging from 30.2% to 47.4% in Western populations and even higher, between 47.7% and 67.6%, in Chinese populations [<xref ref-type="bibr" rid="B8">8</xref>]. The high prevalence and molecular heterogeneity within the NSMP subtype pose significant challenges, as it includes aggressive variants such as dedifferentiated or undifferentiated EC with <italic>SMARCA4</italic> loss, which have poor prognoses despite the absence of <italic>TP53</italic> mutations [<xref ref-type="bibr" rid="B9">9</xref>].</p>
<p>Recent studies have demonstrated excellent concordance between molecular classification methods such as ProMisE and NGS panels, highlighting the potential of NGS for the clinical molecular classification of EC [<xref ref-type="bibr" rid="B10">10</xref>, <xref ref-type="bibr" rid="B11">11</xref>]. NGS offers superior sensitivity compared to traditional Sanger sequencing, particularly for detecting low-frequency <italic>POLE</italic> mutations, which are crucial for accurate subtype classification and subsequent treatment planning [<xref ref-type="bibr" rid="B12">12</xref>]. Moreover, NGS panels can simultaneously identify a wide array of mutations associated with targeted therapy trials, including alterations in genes such as <italic>PIK3CA</italic>, <italic>ARID1A</italic>, <italic>BRCA1/2</italic>, <italic>PTEN</italic>, <italic>KRAS</italic>, <italic>AKT1</italic>, <italic>FBXW7</italic>, and <italic>HER2</italic>, thereby providing valuable information for the prognosis and personalized therapy of patients with advanced EC.</p>
<p>This study aimed to evaluate the efficacy of a single-test targeted DNA NGS panel in classifying EC into molecular subtypes and compare its performance with that of the established Sanger sequencing &#x2b; immunohistochemistry (Sanger &#x2b; IHC) molecular classification. By implementing a clinically validated NGS panel, we sought to streamline the molecular classification process, enhance the sensitivity of mutation detection, and identify additional mutations that may inform the prognosis and therapeutic strategies for patients with advanced EC.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>Materials and methods</title>
<sec id="s2-1">
<title>Patient cohort, sample collection, and pathological information</title>
<p>This retrospective study included 196 patients with EC who underwent surgical resection at our hospital between January 2022 and December 2024. The patients did not receive neoadjuvant chemotherapy or radiotherapy prior to surgery. Formalin-fixed paraffin-embedded (FFPE) tumor samples were retrieved from the Pathology Department Biobank. Two patients were excluded because of sequencing failure or insufficient tumor tissue for DNA extraction, resulting in a final sample size of 194 patients with EC.</p>
<p>Two pathologists performed pathological classification, tumor grading, assessment of myometrial invasion, and lymphovascular space invasion according to the WHO guidelines [<xref ref-type="bibr" rid="B5">5</xref>]. Clinical staging was based on the International Federation of Gynecology and Obstetrics staging system (2023) [<xref ref-type="bibr" rid="B6">6</xref>]. This study was approved by the Ethics Committee of our hospital, and written informed consent was obtained from all participants.</p>
</sec>
<sec id="s2-2">
<title>Immunohistochemistry</title>
<p>Each FFPE tissue sample was sectioned into 3.5&#xa0;&#xb5;m slices for IHC staining using the LUMATAS automatic pathological staining system. The primary antibodies used were from Fuzhou Maixin Biotech (Anti-MLH1, clone MX063; Anti-PMS2, clone MX073; Anti-MSH2, clone MX061; Anti-MSH6, clone MX056; Anti-p53, clone MX008). All histological and IHC slides were independently reviewed by two pathologists.</p>
<p>Normal protein expression of <italic>MLH1</italic>, <italic>PMS2</italic>, <italic>MSH2,</italic> and <italic>MSH6</italic> was defined as normal nuclear expression. Loss of expression was defined as the complete loss of nuclear expression, whereas internal controls (stromal or lymphocytic cells) exhibited strong nuclear staining. Based on the MMR protein expression status, tumors were categorized into two groups: deficient mismatch repair (dMMR) if any of the MMR proteins (<italic>MLH1, PMS2</italic>, <italic>MSH2</italic>, and <italic>MSH6</italic>) were lost, and proficient mismatch repair (pMMR) if all MMR proteins were positively expressed.</p>
<p>p53 expression patterns were classified as overexpression (OE), complete absence (CA), cytoplasmic expression (CY), or wild-type (WT), with OE, CA, and CY constituting abnormal p53 expression patterns. Tumors with p53 abnormalities (p53abn), if &#x2265;75% of tumor cell nuclei exhibited strong or diffuse staining (OE), were completely negative (CA), or showed cytoplasmic staining (CY) [<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B14">14</xref>].</p>
<p>Abnormal subclonal expression was considered to occur in p53 and MMR proteins, if abnormal staining patterns were detected in areas adjacent to the tumor.</p>
</sec>
<sec id="s2-3">
<title>DNA extraction</title>
<p>Paraffin-embedded tumor samples selected by pathological examination were subjected to DNA extraction using the MagPure FFPE DNA LQ Kit (Magen), according to the manufacturer&#x2019;s protocol. Genomic DNA concentration was quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific).</p>
</sec>
<sec id="s2-4">
<title>Sanger sequencing</title>
<p>
<italic>POLE</italic> gene mutations were detected using Sanger sequencing, as previously described [<xref ref-type="bibr" rid="B15">15</xref>]. Exons 9&#x2013;14 of the <italic>POLE</italic> gene were amplified using primers provided by Sangon Biotech (Shanghai) Co., Ltd. Only 11 previously identified pathogenic variants (P286R, V411L, S297F, S459F, A456P, F367S, L424I, M295R, P436R, M444K, and D368Y) were targeted [<xref ref-type="bibr" rid="B16">16</xref>]. PCR products were confirmed by gel electrophoresis, purified using the MinElute PCR Purification Kit (QIAGEN) according to the manufacturer&#x2019;s instructions, and subsequently subjected to sequencing using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems). Sequencing reactions were purified and analyzed using a SeqStudio Genetic Analyzer (Applied Biosystems). All <italic>POLE</italic> mutations were annotated according to the LRG_789 (NM_006231.3) and Human Genome Variation Society (HGVS) guidelines.</p>
</sec>
<sec id="s2-5">
<title>Next-generation sequencing</title>
<p>Targeted DNA NGS was performed on 131 samples using the clinically validated AmoyDx&#xae; Comprehensive Panel (Amoy Diagnostics, Xiamen, China), which comprises 116 cancer-related genes, including 109 genes for single nucleotide variants (SNVs), insertions/deletions (indels), 31 genes for copy number variations (CNVs), and 12 genes for gene fusions. The panel covers mutations in <italic>POLE, TP53, PTEN, PIK3CA, ARID1A, KRAS, HER2, BRCA1, BRCA2</italic>, and <italic>SMARCA4</italic> and includes microsatellite instability (MSI) analysis. Genes and their detection regions are listed in <xref ref-type="sec" rid="s11">Supplementary Table S1</xref>. MSI status was determined based on the percentage of unstable loci out of 55 microsatellite loci: samples with &#x3c;15% unstable loci were classified as microsatellite stable (MSS), and those with &#x2265;15% were classified as MSI-high (MSI-H). A commercially available targeted AmoyDx EC panel covering <italic>POLE, TP53</italic>, and MSI was used for 63 samples in this study (Amoy Diagnostics, Xiamen, China).</p>
<p>Sequencing was performed on an Illumina NextSeq 500 platform (Illumina) with the following quality criteria: Q30 base percentage &#x2265;75%, coverage &#x2265;95%, CoverageRatioUNIQ180 (proportion of hotspot regions with effective sequencing depth &#x2265;180&#xd7;) &#x2265;95%, cnv_cv (coefficient of variation for sequencing depth within gene regions) &#x3c;0.4, and cnv_uni (degree of deviation from the panel of normals) &#x3c;1.5 (see <xref ref-type="sec" rid="s11">Supplementary Tables S2, S3</xref>). The mutation allele frequency (MAF) thresholds for mutation identification by NGS were set as follows: SNVs and insertions/deletions (indels) &#x2265;5% and CNVs &#x2265;4 copies. Sequencing data were analyzed using bioinformatics software, and the therapeutic relevance of the identified genetic variants was interpreted based on publicly available databases, including the NCCN Guidelines and OncoKB [<xref ref-type="bibr" rid="B17">17</xref>]. The evidence levels for variant-drug associations were categorized into four tiers (A-D) according to the AMP/ASCO/CAP guidelines [<xref ref-type="bibr" rid="B18">18</xref>]: Tier A (FDA-approved or supported by professional clinical guidelines), Tier B (validated by large-scale clinical studies with expert consensus), Tier C (supported by Tier A evidence in other cancer types, used as inclusion criteria in clinical trials, or supported by multiple small-scale studies), and Tier D (preclinical studies or case reports).</p>
</sec>
<sec id="s2-6">
<title>Bisulfite treatment and methylation-specific PCR</title>
<p>Bisulfite conversion of purified DNA was performed using the EpiTect Fast DNA Bisulfite Kit (Qiagen), following the manufacturer&#x2019;s protocol. PCR products were analyzed using 3% agarose gel electrophoresis to verify successful conversion and amplification (<xref ref-type="sec" rid="s11">Supplementary Figure S1</xref>).</p>
</sec>
<sec id="s2-7">
<title>WHO molecular classification and NGS molecular classification</title>
<p>Molecular classification was performed according to the WHO guidelines [<xref ref-type="bibr" rid="B5">5</xref>]. Patients with one of the 11 confirmed pathogenic <italic>POLE</italic> variants detected by Sanger sequencing were categorized as POLEmut. The remaining EC cases were classified based on MMR status, with tumors showing loss of one or more MMR proteins designated as MMRd, and those with intact MMR protein expression classified as pMMR. Subsequently, EC cases were further stratified based on p53 status, where abnormal p53 expression patterns (OE, CA, and CY) were categorized as p53abn, and normal p53 expression (WT) was categorized as NSMP.</p>
<p>For NGS-based classification, patients with one of the 11 confirmed pathogenic <italic>POLE</italic> variants identified by the NGS panel were grouped as NGS POLEmut. The remaining EC cases were classified based on MSI status, with MSI-H cases categorized as NGS MSI-H/MMRd, and MSS cases further stratified based on <italic>TP53</italic> mutation status. Tumors harboring <italic>TP53</italic> mutations were designated NGS TP53mut/p53abn, whereas those without <italic>TP53</italic> mutations were classified as NGS NSMP/TP53wt.</p>
</sec>
<sec id="s2-8">
<title>Statistical analysis</title>
<p>The consistency between Sanger &#x2b; IHC and NGS molecular classifications was evaluated using overall accuracy and the kappa coefficient. Associations between molecular subgroups and clinicopathological characteristics were assessed using appropriate statistical tests, including the &#x3c7;<sup>2</sup> test for categorical variables and analysis of variance (ANOVA) for continuous variables. All statistical analyses were two-tailed, with a significance set at <italic>P</italic> &#x3c; 0.05. Data analysis was performed using the IBM SPSS Statistics software (version 19.0; IBM Corp., Armonk, NY, USA).</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec id="s3-1">
<title>Descriptive statistics of EC patients</title>
<p>A total of 194 endometrial carcinoma specimens were successfully to molecular classification using NGS. The clinical and pathological characteristics of the patients are shown in <xref ref-type="table" rid="T1">Table 1</xref>. Based on the NGS molecular classification, 15.5% (30/194) of the cases were categorized as NGS POLEmut, 23.2% (45/194) as NGS MSI-H/MMRd, 14.4% (28/194) as NGS TP53mut/p53abn, and 46.9% (91/194) as NGS NSMP/TP53wt. Notably, the NGS classification identified 10.3% (20/194) of cases with multiple molecular features. Among these, ten NGS POLEmut tumors harbored <italic>TP53</italic> mutations, with one case exhibiting three distinct molecular features (POLEmut, MSI-H, and TP53mut), and ten NGS MSI-H/MMRd tumors concurrently displayed <italic>TP53</italic> mutations.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Descriptive statistics of patients by NGS molecular classification subgroups based on post-operative specimens.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Patient characteristics</th>
<th align="center">Total</th>
<th align="center">NGS POLEmut</th>
<th align="center">NGS MSI-H/MMRd</th>
<th align="center">NGS TP53mut/p53abn</th>
<th align="center">NGS NSMP/TP53wt</th>
<th align="center">
<italic>p</italic>-value</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Age at surgery</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.006</td>
</tr>
<tr>
<td align="center">Mean(SD)</td>
<td align="center">58.4 (&#xb1;9.7)</td>
<td align="center">55.2 (&#xb1;8.0)</td>
<td align="center">60.8 (&#xb1;8.6)</td>
<td align="center">62.4 (&#xb1;7.9)</td>
<td align="center">57.1 (&#xb1;10.6)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Median</td>
<td align="center">58</td>
<td align="center">56</td>
<td align="center">58</td>
<td align="center">62</td>
<td align="center">57</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Grade</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">&#x3c;0.001</td>
</tr>
<tr>
<td align="center">G1</td>
<td align="center">60 (31.9%)</td>
<td align="center">8 (27.6%)</td>
<td align="center">10 (22.7%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">42 (46.7%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">G2</td>
<td align="center">82 (43.6%)</td>
<td align="center">11 (37.9%)</td>
<td align="center">22 (50.0%)</td>
<td align="center">5 (20.0%)</td>
<td align="center">44 (48.9%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">G3</td>
<td align="center">46 (24.5%)</td>
<td align="center">10 (34.5%)</td>
<td align="center">12 (27.3%)</td>
<td align="center">20 (80.0%)</td>
<td align="center">4 (4.4%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Histological subtype</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">&#x3c;0.001</td>
</tr>
<tr>
<td align="center">Endometrioid (EM)</td>
<td align="center">170 (89.0%)</td>
<td align="center">25 (83.3%)</td>
<td align="center">40 (90.9%)</td>
<td align="center">15 (57.7%)</td>
<td align="center">90 (98.9%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Clear cell</td>
<td align="center">7 (3.7%)</td>
<td align="center">2 (6.7%)</td>
<td align="center">1 (2.3%)</td>
<td align="center">4 (15.4%)</td>
<td align="center">0 (0.0%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Serous</td>
<td align="center">6 (3.1%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">6 (23.1%)</td>
<td align="center">0 (0.0%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Mixed EM and serous</td>
<td align="center">2 (1.0%)</td>
<td align="center">2 (6.7%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">0 (0.0%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Dedifferentiated</td>
<td align="center">3 (1.6%)</td>
<td align="center">1 (3.3%)</td>
<td align="center">1 (2.3%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">1 (1.1%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Mixed EM and carcinosarcoma</td>
<td align="center">1 (0.5%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">1 (3.8%)</td>
<td align="center">0 (0.0%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Mixed serous and carcinosarcoma</td>
<td align="center">2 (1.0%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">2 (4.5%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">0 (0.0%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Stage</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.135</td>
</tr>
<tr>
<td align="center">I</td>
<td align="center">124 (66.0%)</td>
<td align="center">22 (73.3%)</td>
<td align="center">31 (70.5%)</td>
<td align="center">9 (37.5%)</td>
<td align="center">62 (68.9%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">II</td>
<td align="center">24 (12.8%)</td>
<td align="center">2 (6.7%)</td>
<td align="center">4 (9.1%)</td>
<td align="center">5 (20.8%)</td>
<td align="center">13 (14.4%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">III</td>
<td align="center">35 (18.6%)</td>
<td align="center">6 (20.0%)</td>
<td align="center">8 (18.2%)</td>
<td align="center">8 (33.3%)</td>
<td align="center">13 (14.4%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">IV</td>
<td align="center">5 (2.7%)</td>
<td align="center">0 (0.0%)</td>
<td align="center">1 (2.3%)</td>
<td align="center">2 (8.3%)</td>
<td align="center">2 (2.2%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Myometrial invasion</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.043</td>
</tr>
<tr>
<td align="center">None</td>
<td align="center">18 (9.6%)</td>
<td align="center">7 (23.3%)</td>
<td align="center">4 (9.1%)</td>
<td align="center">3 (12.5%)</td>
<td align="center">4 (4.5%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">&#x3c;50%</td>
<td align="center">105 (56.1%)</td>
<td align="center">17 (56.7%)</td>
<td align="center">23 (52.3%)</td>
<td align="center">10 (41.7%)</td>
<td align="center">55 (61.8%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">&#x2265;50%</td>
<td align="center">64 (34.2%)</td>
<td align="center">6 (20.0%)</td>
<td align="center">17 (38.6%)</td>
<td align="center">11 (45.8%)</td>
<td align="center">30 (33.7%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">LVSI</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.013</td>
</tr>
<tr>
<td align="center">Yes</td>
<td align="center">125 (67.2%)</td>
<td align="center">22 (73.3%)</td>
<td align="center">23 (52.3%)</td>
<td align="center">12 (52.2%)</td>
<td align="center">68 (76.4%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">No</td>
<td align="center">61 (32.8%)</td>
<td align="center">8 (26.7%)</td>
<td align="center">21 (47.7%)</td>
<td align="center">11 (47.8%)</td>
<td align="center">21 (23.6%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">Lymph node involvement</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.592</td>
</tr>
<tr>
<td align="center">Yes</td>
<td align="center">163 (88.1%)</td>
<td align="center">27 (90.0%)</td>
<td align="center">37 (84.1%)</td>
<td align="center">20 (83.3%)</td>
<td align="center">79 (90.8%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">No</td>
<td align="center">22 (11.9%)</td>
<td align="center">3 (10.0%)</td>
<td align="center">7 (15.9%)</td>
<td align="center">4 (16.7%)</td>
<td align="center">8 (9.2%)</td>
<td align="left">&#x200b;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>NOTE: the total number of cases for each parameter may be less than the overall total because of missing data.</p>
</fn>
<fn>
<p>Abbreviations: LVSI, lymphovascular space invasion; POLEmut, POLE, ultramutated; MMRd, mismatch repair deficient; p53abn, p53 abnormal; NSMP, non-specific molecular profile. MSI-H, high microsatellite instability.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>In comparison, the Sanger &#x2b; IHC molecular classification assigned 11.9% (23/194) of cases to the POLEmut category, 23.7% (46/194) to MMRd, 13.9% (27/194) to p53abn, and 50.5% (98/194) to NSMP. The Sanger &#x2b; IHC classification identified ten cases with overlapping molecular characteristics: five POLEmut tumors showed abnormal p53 expression or deficient mismatch repair (dMMR), and five MMRd tumors concurrently exhibited abnormal p53 expression.</p>
</sec>
<sec id="s3-2">
<title>Comparison between NGS and sanger &#x2b; IHC molecular classifications</title>
<p>
<xref ref-type="fig" rid="F1">Figure 1</xref> presents the molecular marker results for both Sanger &#x2b; IHC and NGS classifications. A comparative analysis revealed that 93.8% (182/194) of the cases showed concordance between the NGS and Sanger &#x2b; IHC molecular classifications, achieving a kappa coefficient of 0.908 and an overall accuracy of 0.938, (<xref ref-type="fig" rid="F2">Figures 2</xref>, <xref ref-type="fig" rid="F3">3</xref>). However, there were 12 discordant cases between the two classification methods (<xref ref-type="table" rid="T2">Table 2</xref>). Specifically, cases 1 and 2 were classified as NSMP by Sanger &#x2b; IHC but as NGS TP53mut/p53abn, with <italic>TP53</italic> variants S260Qfs&#x2a;3 and G245S detected at a MAF of 2.4% and 15%, respectively. Both cases had tumor cell contents exceeding 50%, suggesting tumor heterogeneity, with a low proportion of <italic>TP53</italic>-mutated cells. Cases 3 and 4 were classified as MMRd by Sanger &#x2b; IHC but as NGS NSMP/TP53wt, with no variants detected in the <italic>MSH2, MSH6, MLH1, PMS2,</italic> or <italic>EPCAM</italic> genes. Additionally, seven cases with <italic>POLE</italic> mutations were missed by Sanger sequencing because of their lower sensitivity than NGS, particularly for variants with a MAF &#x3c;10%. After excluding the seven POLEmut cases that were missed by Sanger sequencing, the concordance between NGS and Sanger &#x2b; IHC molecular classifications increased to 97.4% (189/194), with a kappa value of 0.962.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Representative images depicting Sanger &#x2b; IHC and NGS molecular classification. <bold>(A)</bold> Sanger &#x2b; IHC molecular classification (<italic>POLE</italic> mutation detected by Sanger sequencing and MMR and p53 mutations detected by IHC). <bold>(B)</bold> NGS molecular classification (detection of <italic>POLE</italic> mutations, MSI status, and <italic>TP53</italic> mutations using NGS). Abbreviations: POLEmut - POLE-Mutated; MMRd - Mismatch Repair-Deficient; p53abn - p53 abnormal; NSMP - No Specific Molecular Profile; MSI-H - high microsatellite instability; TP53mut - Tp53-Mutated; TP53wt - Tp53 wild-type.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="pore-32-1612465-g001.tif">
<alt-text content-type="machine-generated">Panel A contains three columns comparing POLE sequencing chromatograms, MMR immunohistochemistry, and P53 immunohistochemistry across four molecular subtypes: POLE mut, MMRd, p53abn, and NSMP. Panel B displays genome browser snapshots and microsatellite instability box plots for POLE, MSI, and TP53 across corresponding molecular subtypes, highlighting differences in mutation and expression patterns.</alt-text>
</graphic>
</fig>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Molecular subtype distribution of prospectively accrued endometrial based on NGS and Sanger &#x2b; IHC molecular classification. <bold>(A)</bold> This study included all 194 prospectively accrued endometrial carcinomas, and bar plots depict the molecular subtype distribution according to NGS and Sanger &#x2b; IHC molecular classifications. <bold>(B)</bold> Concordance between NGS and Sanger &#x2b; IHC molecular classifications.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="pore-32-1612465-g002.tif">
<alt-text content-type="machine-generated">Bar chart comparing four molecular subgroups of endometrial cancer by Sanger plus immunohistochemistry and next-generation sequencing methods, with subgroup frequencies ranging from approximately 12% to 50%. Matrix table below shows concordance between methods across subgroups, with kappa statistic of 0.908.</alt-text>
</graphic>
</fig>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Oncoprint of genomic alterations in the four molecular subgroups of the cohort (n &#x3d; 194). Endometrial cancers classified according to NGS or Sanger &#x2b; IHC molecular classification are shown as the NGS and Sanger &#x2b; IHC groups, respectively. Both NGS-based and Sanger-based <italic>POLE</italic> mutations are shown in the figure. Both NGS-based MSI status and IHC-based MMR are shown in the figure, as are the results of the NGS-based <italic>TP53</italic> mutations and IHC-based p53 assessment. Abbreviations: POLEmut - POLE-Mutated; MMRd - Mismatch Repair-Deficient; p53abn - p53 abnormal; NSMP - No Specific Molecular Profile; MSI-H - high microsatellite instability; TP53mut - Tp53-Mutated; TP53wt - Tp53 wild-type.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="pore-32-1612465-g003.tif">
<alt-text content-type="machine-generated">Multicolored horizontal stacked bar chart visualizing patient-level data for endometrial cancer, showing variables like molecular subgroup, mutation status, age, tumor grade, stage, histological subtype, lymph node involvement, lymphovascular space invasion, and myometrial invasion. Each row represents a clinical or molecular parameter, color-coded according to a key on the same image, with distinct color blocks denoting values such as group, mutation type, age, tumor grade or stage, and histological diagnosis. The legend explains all colors and categorizations used.</alt-text>
</graphic>
</fig>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Cases with discordance between original Sanger &#x2b; IHC and NGS panel classifications.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Case</th>
<th align="left">Sanger &#x2b; IHC</th>
<th align="left">NGS panel</th>
<th align="left">Possible explanations for discordance</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">1</td>
<td align="left">NSMP</td>
<td align="left">NGS TP53mut/p53abn</td>
<td rowspan="2" align="left">It is speculated that the tumor is heterogeneous, and the proportion of tumor cells with a <italic>TP53</italic> gene mutation is very low</td>
</tr>
<tr>
<td align="left">2</td>
<td align="left">NSMP</td>
<td align="left">NGS TP53mut/p53abn</td>
</tr>
<tr>
<td align="left">3</td>
<td align="left">MMRd</td>
<td align="left">NGS NSMP/TP53wt</td>
<td rowspan="2" align="left">Due to MLH1 promoter methylation</td>
</tr>
<tr>
<td align="left">4</td>
<td align="left">MMRd</td>
<td align="left">NGS NSMP/TP53wt</td>
</tr>
<tr>
<td align="left">5</td>
<td align="left">NSMP</td>
<td align="left">MSI-H/MMRd</td>
<td align="left">False-positive staining may occur in cases of amino acid substitutions that lead to loss of function with preserved immunoreactive protein expression</td>
</tr>
<tr>
<td align="left">6</td>
<td align="left">NSMP</td>
<td align="left">NGS POLEmut</td>
<td rowspan="7" align="left">Difference in detection sensitivity between NGS and sanger sequencing technology</td>
</tr>
<tr>
<td align="left">7</td>
<td align="left">NSMP</td>
<td align="left">NGS POLEmut</td>
</tr>
<tr>
<td align="left">8</td>
<td align="left">NSMP</td>
<td align="left">NGS POLEmut</td>
</tr>
<tr>
<td align="left">9</td>
<td align="left">NSMP</td>
<td align="left">NGS POLEmut</td>
</tr>
<tr>
<td align="left">10</td>
<td align="left">NSMP</td>
<td align="left">NGS POLEmut</td>
</tr>
<tr>
<td align="left">11</td>
<td align="left">NSMP</td>
<td align="left">NGS POLEmut</td>
</tr>
<tr>
<td align="left">12</td>
<td align="left">NSMP</td>
<td align="left">NGS POLEmut</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3-3">
<title>Consistency between NGS and sanger sequencing for <italic>POLE</italic> detection</title>
<p>As shown in <xref ref-type="fig" rid="F4">Figure 4</xref>, NGS identified 30 <italic>POLE</italic> gene mutations, including P286R in 15 cases, V411L in 11 cases, A456P in two cases, and single instances of S297F and P436S. In contrast, Sanger sequencing detected only 23 <italic>POLE</italic> mutations, excluding seven cases with allelic frequencies &#x3c;10% (<xref ref-type="sec" rid="s11">Supplementary Table S4</xref>). These seven cases, which had low tumor content (20%), underwent tumor region delineation and enrichment based on hematoxylin and eosin (HE) staining prior to DNA extraction and subsequent Sanger sequencing. Remarkably, all seven cases were confirmed to harbor <italic>POLE</italic> mutations (<xref ref-type="fig" rid="F5">Figure 5</xref>).</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Comparison between NGS, Sanger sequencing, and immunohistochemistry. Bar plots show the concordance between <bold>(A)</bold> NGS and Sanger sequencing technology for <italic>POLE</italic> mutation analysis; <bold>(B)</bold> quantitative analysis of <italic>POLE</italic> mutations using NGS and Sanger sequencing technology; <bold>(C)</bold> NGS-based MSI status detection and MMR Immunohistochemistry; <bold>(D)</bold> quantitative analysis of NGS-based MSI status and MMR Immunohistochemistry and <bold>(E)</bold> NGS-based <italic>TP53</italic> mutation analysis and p53 immunohistochemistry. Table of concordance between <bold>(F)</bold> quantitative analysis of NGS-based <italic>TP53</italic> mutations and p53 immunohistochemistry.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="pore-32-1612465-g004.tif">
<alt-text content-type="machine-generated">Six-panel scientific figure comparing methods for detecting genetic mutations and status in 194 cases: Panels A, C, and E show bar graphs of percentages for POLE, MSI/MMR, and TP53 status using NGS and IHC methods, with color-coded mutation types. Panels B, D, and F present contingency tables comparing mutation or status results between NGS and either Sanger sequencing or IHC, showing sample counts, percentages, kappa values, and statistical significance.</alt-text>
</graphic>
</fig>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>FFPE enrichment and resequencing of endometrial cancers were performed in 7 cases that were not detected by Sanger sequencing for POLE. <bold>(A)</bold> Seven original endometrial cancer samples with low tumor content. <bold>(B)</bold> HE staining was used to mark the tumor area and enrich the tumor content. <bold>(C)</bold> Enriched endometrial cancer tumor cell morphology. <bold>(D)</bold> <italic>POLE</italic> mutation detected by Sanger sequencing.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="pore-32-1612465-g005.tif">
<alt-text content-type="machine-generated">Grid of microscopy and analytical images for seven cases labeled Case 1 to Case 7, with columns A to D. Column A shows stained tissue sections marked with black outlines, column B shows smaller tissue fragments, column C presents high-magnification microscopic images of tissue morphology, and column D displays sequencing chromatograms with distinct peaks for each case.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-4">
<title>Concordance between NGS-based MSI status and IHC-based MMR status</title>
<p>IHC for MLH1, MSH2, MSH6, and PMS2 proteins, along with PCR-based MSI analysis, are the standard methods for assessing MSI phenotypes in EC molecular classification. The NGS-based MSI status, determined by analyzing the read distribution across 55 microsatellite loci, was compared with the IHC-based MMR status. The results demonstrated high concordance, with 97.9% (190/194) of cases being consistent between the two methods. Four discordant cases were observed: three IHC-diagnosed dMMR cases were classified as MSS by NGS, and one IHC-diagnosed pMMR case was classified as MSI-H by NGS. These three cases exhibited concurrent loss of MLH1 and PMS2 expression and <italic>MLH1</italic> promoter hypermethylation (<xref ref-type="sec" rid="s11">Supplementary Figure S1</xref>). Additionally, two cases displayed partial loss of MMR protein expression: one with a 50% loss of MLH1/PMS2, and the other with a 20% loss of MLH1/PMS2, indicating a subclonal staining pattern (<xref ref-type="sec" rid="s11">Supplementary Figure S2</xref>), and both were positive for <italic>MLH1</italic> promoter methylation.</p>
<p>Among the 194 patients, 45 had MSI-H. Owing to tumor-only testing, the germline mutation status remained uncertain. Although the NGS panel also detected <italic>MLH1, PMS2, MSH2,</italic> and <italic>MSH6</italic> gene variants, no pathogenic MMR-related gene mutations were identified in the dMMR tumors. No mutations in <italic>EPCAM</italic> were detected in the 194 patients. Germline testing for Lynch syndrome is typically recommended. However, no mutations were detected in this cohort.</p>
</sec>
<sec id="s3-5">
<title>Concordance between NGS-based <italic>TP53</italic> mutation detection and IHC-Based p53 status</title>
<p>Both NGS and IHC were used to determine p53 status in all EC cases in this study. Abnormal p53 status was defined by IHC as &#x2265;75% of tumor cell nuclei exhibiting strong or diffuse staining, complete absence of nuclear staining, or cytoplasmic staining. Three cases had p53 expression levels near the threshold; 60%, 65%, and 70% of tumor cells showed moderate positive expression, and were thus classified as p53 wild-type (WT) according to the interpretation criteria.</p>
<p>NGS detected at least one potentially pathogenic <italic>TP53</italic> mutation in 48 of the 194 cases, including variants such as V73Wfs&#x2a;50, C141Y, R181C, R213&#x2a;, C238Y, S241F, G245S, R248W, R248Q, T253A, L257R, ES260Qfs&#x2a;3, R267W, R273C, R273H, P278S, R282W, and E285Gfs&#x2a;20. Additionally, two novel <italic>TP53</italic> variants (R202_V203del and V157_M160del) were identified using NGS, both of which were associated with mutant p53 expression levels.</p>
<p>
<xref ref-type="fig" rid="F4">Figure 4</xref> highlights that 18 cases with <italic>TP53</italic> mutations detected by NGS were classified as p53 wild-type by IHC, resulting in a moderate concordance with a kappa coefficient of 0.688 and an overall concordance rate of 89.7% (174/194 cases). When POLEmut and MSI-H/MMRd cases were excluded, in which <italic>TP53</italic> mutations were often considered passenger mutations that did not influence the classification, the consistency between IHC-based p53 staining and NGS-based <italic>TP53</italic> mutation detection improved significantly. Among the remaining 119 cases, only three showed discordances, leading to an overall concordance rate of 97.5% (116/119 cases), as presented in <xref ref-type="table" rid="T3">Table 3</xref>.</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Comparison of molecular subtype diagnoses by NGS and IHC for p53.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">NGS panel</th>
<th colspan="3" align="center">p53 IHC</th>
</tr>
<tr>
<th align="center">NSMP</th>
<th align="center">p53abn</th>
<th align="center">Total</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">NSMP/TP53wt</td>
<td align="center">91 (76.5%)</td>
<td align="center">0 (0%)</td>
<td align="center">91 (76.5%)</td>
</tr>
<tr>
<td align="center">TP53mut/p53abn</td>
<td align="center">3 (2.5%)</td>
<td align="center">25 (21.0%)</td>
<td align="center">28 (23.5%)</td>
</tr>
<tr>
<td align="center">Total</td>
<td align="center">94 (79.0%)</td>
<td align="center">25 (21.0%)</td>
<td align="center">119 (100%)</td>
</tr>
<tr>
<td colspan="4" align="left" style="background-color:#FFFFFF">Kappa &#x3d; 0.927 (95% CI 0.830&#x2013;1.000) P &#x3c; 0.001</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Subclonal p53 expression (defined as mutant-pattern staining confined to discrete tumor foci occupying &#x3c;10% of the total tumor area) was observed in 10/194 (5.2%) cases (<xref ref-type="sec" rid="s11">Supplementary Figure S3</xref>). TP53 mutations were detected in 5/10 cases, all of which met the NGS detection threshold (MAF&#x2265;5%). Among these 10 tumors, five were molecularly classified as either mismatch repair deficient (MMRd, n &#x3d; 1) or polymerase epsilon-mutated (POLEmut, n &#x3d; 4). Notably, all five MMRd/POLEmut tumors predominantly exhibited wild-type p53 expression patterns with only focal mutant-pattern staining. TP53 mutations were identified in 4/5 cases, suggesting potential discordance between molecular and immunohistochemical (IHC) results due to either low tumor cellularity in sampled regions or subclonal mutations below the IHC detection threshold.</p>
</sec>
<sec id="s3-6">
<title>Mutations associated with targeted therapy trials detected by NGS</title>
<p>Forty-two mutations associated with targeted therapy trials were identified in 131 samples (<xref ref-type="fig" rid="F6">Figure 6</xref>). The most frequently mutated genes were <italic>PTEN</italic> (76.3%), <italic>PIK3CA</italic> (50.4%), and <italic>ARID1A</italic> (35.9%). Beyond the primary molecular markers <italic>POLE</italic>, <italic>TP53</italic>, and MSI, additional mutations with frequencies exceeding 10% included <italic>KRAS</italic> (25.2%) and <italic>CTNNB1</italic> (17.6%).</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Repertoires of genetic alterations across histological subtypes of prospectively acquired endometrial cancer. Mutations and gene amplification were identified by sequencing, targeting 116 cancer-related genes in 131 prospectively accrued endometrial cancer cases. The cases are shown in columns, and the genes in rows. Tumor type, molecular classification, and variations are color-coded according to the legend.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="pore-32-1612465-g006.tif">
<alt-text content-type="machine-generated">Oncoprint heatmap summarizing gene variations in endometrial cancer samples, with colored bars for FIGO grade, FIGO stage, age, tumor type, and molecular classification across the top, matched to color-coded keys on the right. Genes are listed vertically, with green squares indicating SNV/InDel, blue for MSI status, and yellow for amplification events. Each vertical column represents an individual sample.</alt-text>
</graphic>
</fig>
<p>The distribution of specific gene mutations varied significantly across the FIGO stages (<xref ref-type="table" rid="T4">Table 4</xref>). For example, <italic>PTEN</italic> mutations decreased from 86.8% in FIGO G1 to 59.3% in FIGO G3 (P &#x3c; 0.05), whereas <italic>PIK3CA</italic> mutations decreased from 60.5% in G1 to 29.6% in G3 (P &#x3c; 0.05). Conversely, <italic>TP53</italic> mutations increased markedly from 2.6% in G1 to 59.3% in G3 (P &#x3c; 0.001) and <italic>HER2</italic> mutations increased from 0% in G1 to 11.4% in G3 (P &#x3c; 0.05).</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Statistical analysis of gene alterations associated with targeted therapy trials in 131 prospectively accrued endometrial cancer cases.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="2" align="center">Patient characteristics</th>
<th align="center">PTEN</th>
<th align="center">
<italic>p</italic> value</th>
<th align="center">PIK3CA</th>
<th align="center">
<italic>p</italic> value</th>
<th align="center">ARID1A</th>
<th align="center">
<italic>p</italic> value</th>
<th align="center">KRAS</th>
<th align="center">
<italic>p</italic> value</th>
<th align="center">CTNNB1</th>
<th align="center">P value</th>
<th align="center">TP53</th>
<th align="center">
<italic>p</italic> value</th>
<th align="center">HER2</th>
<th align="center">
<italic>p</italic> value</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">&#x200b;</td>
<td align="center">Total</td>
<td align="center">100 (76.3%)</td>
<td align="left">&#x200b;</td>
<td align="center">66 (50.4%)</td>
<td align="left">&#x200b;</td>
<td align="center">47 (35.9%)</td>
<td align="left">&#x200b;</td>
<td align="center">33 (25.2%)</td>
<td align="left">&#x200b;</td>
<td align="center">23 (17.6%)</td>
<td align="left">&#x200b;</td>
<td align="center">30 (22.9%)</td>
<td align="left">&#x200b;</td>
<td align="center">4 (3.1%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Grade</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.036</td>
<td align="left">&#x200b;</td>
<td align="center">0.043</td>
<td align="left">&#x200b;</td>
<td align="center">0.132</td>
<td align="left">&#x200b;</td>
<td align="center">0.110</td>
<td align="left">&#x200b;</td>
<td align="center">0.085</td>
<td align="left">&#x200b;</td>
<td align="center">&#x3c;0.001</td>
<td align="left">&#x200b;</td>
<td align="center">0.027</td>
</tr>
<tr>
<td align="center">G1</td>
<td align="center">38</td>
<td align="center">33 (86.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">23 (60.5%)</td>
<td align="left">&#x200b;</td>
<td align="center">13 (34.2%)</td>
<td align="left">&#x200b;</td>
<td align="center">14 (36.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">8 (21.1%)</td>
<td align="left">&#x200b;</td>
<td align="center">1 (2.6%)</td>
<td align="left">&#x200b;</td>
<td align="center">0</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">G2</td>
<td align="center">61</td>
<td align="center">47 (77.0%)</td>
<td align="left">&#x200b;</td>
<td align="center">32 (52.5%)</td>
<td align="left">&#x200b;</td>
<td align="center">27 (44.3%)</td>
<td align="left">&#x200b;</td>
<td align="center">14 (23.0%)</td>
<td align="left">&#x200b;</td>
<td align="center">14 (23.0%)</td>
<td align="left">&#x200b;</td>
<td align="center">9 (14.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">1 (1.6%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">G3</td>
<td align="center">27</td>
<td align="center">16 (59.3%)</td>
<td align="left">&#x200b;</td>
<td align="center">8 (29.6%)</td>
<td align="left">&#x200b;</td>
<td align="center">6 (22.2%)</td>
<td align="left">&#x200b;</td>
<td align="center">4 (14.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">1 (3.7%)</td>
<td align="left">&#x200b;</td>
<td align="center">16 (59.3%)</td>
<td align="left">&#x200b;</td>
<td align="center">3 (11.4%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Age</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.018</td>
<td align="left">&#x200b;</td>
<td align="center">0.667</td>
<td align="left">&#x200b;</td>
<td align="center">0.507</td>
<td align="left">&#x200b;</td>
<td align="center">0.573</td>
<td align="left">&#x200b;</td>
<td align="center">0.313</td>
<td align="left">&#x200b;</td>
<td align="center">0.073</td>
<td align="left">&#x200b;</td>
<td align="center">0.431</td>
</tr>
<tr>
<td align="center">&#x3c;58</td>
<td align="center">58</td>
<td align="center">50 (86.2%)</td>
<td align="left">&#x200b;</td>
<td align="center">28 (48.3%)</td>
<td align="left">&#x200b;</td>
<td align="center">19 (32.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">16 (27.6%)</td>
<td align="left">&#x200b;</td>
<td align="center">8 (13.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">9 (15.5%)</td>
<td align="left">&#x200b;</td>
<td align="center">1 (1.7%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">&#x2265;58</td>
<td align="center">73</td>
<td align="center">50 (68.5%)</td>
<td align="left">&#x200b;</td>
<td align="center">38 (52.1%)</td>
<td align="left">&#x200b;</td>
<td align="center">28 (38.4%)</td>
<td align="left">&#x200b;</td>
<td align="center">17 (23.3%)</td>
<td align="left">&#x200b;</td>
<td align="center">15 (20.5%)</td>
<td align="left">&#x200b;</td>
<td align="center">21 (28.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">3 (4.1%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Stage</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.031</td>
<td align="left">&#x200b;</td>
<td align="center">0.464</td>
<td align="left">&#x200b;</td>
<td align="center">0.110</td>
<td align="left">&#x200b;</td>
<td align="center">0.288</td>
<td align="left">&#x200b;</td>
<td align="center">0.195</td>
<td align="left">&#x200b;</td>
<td align="center">0.031</td>
<td align="left">&#x200b;</td>
<td align="center">0.003</td>
</tr>
<tr>
<td align="center">I-II</td>
<td align="center">104</td>
<td align="center">83 (79.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">54 (51.9%)</td>
<td align="left">&#x200b;</td>
<td align="center">41 (39.4%)</td>
<td align="left">&#x200b;</td>
<td align="center">25 (24.0%)</td>
<td align="left">&#x200b;</td>
<td align="center">21 (20.2%)</td>
<td align="left">&#x200b;</td>
<td align="center">19 (18.3%)</td>
<td align="left">&#x200b;</td>
<td align="center">1 (1.0%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="center">III-IV</td>
<td align="center">23</td>
<td align="center">14 (60.9%)</td>
<td align="left">&#x200b;</td>
<td align="center">10 (43.5%)</td>
<td align="left">&#x200b;</td>
<td align="center">5 (21.7%)</td>
<td align="left">&#x200b;</td>
<td align="center">8 (34.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">2 (8.7%)</td>
<td align="left">&#x200b;</td>
<td align="center">8 (34.8%)</td>
<td align="left">&#x200b;</td>
<td align="center">3 (13.0%)</td>
<td align="left">&#x200b;</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Age-related differences were also notable; patients aged &#x2265;58 years exhibited significantly higher frequencies of <italic>TP53</italic> mutations (28.8% vs. 15.5%, <italic>P</italic> &#x3e; 0.05), <italic>CTNNB1</italic> mutations (20.5% vs13.8%, <italic>P</italic> &#x3e; 0.05), and <italic>HER2</italic> mutations (4.1% vs. 1.7%, <italic>P</italic> &#x3e; 0.05) than those aged &#x3c;58 years.</p>
<p>In advanced EC (FIGO Stage III-IV), mutations in <italic>PTEN</italic> (60.9% vs. 79.8% in early stages, P &#x3c; 0.05), <italic>PIK3CA</italic> (43.5% vs. 51.9%, <italic>P</italic> &#x3e; 0.05), <italic>ARID1A</italic> (21.7% vs. 39.4%, <italic>P</italic> &#x3e; 0.05), <italic>CTNNB1</italic> (8.7% vs. 20.2%, <italic>P</italic> &#x3e; 0.05), <italic>FBXW7</italic> (0% vs. 12.5%, <italic>P</italic> &#x3e; 0.05), and <italic>FGFR2</italic> (4.3% vs. 11.5%, <italic>P</italic> &#x3e; 0.05) were significantly less frequent than in early-stage disease. Conversely, <italic>HER2</italic> mutations were significantly more frequent in patients with advanced EC (13.0% vs. 1.0%, <italic>P</italic> &#x3c; 0.01).</p>
<p>Histological subtype-specific findings revealed that all four clear-cell EC cases harbored <italic>TP53</italic> mutations, as did both serous EC cases, with one also exhibiting <italic>HER2</italic> amplification, and the other presenting a <italic>BRCA1</italic> mutation alongside MYC amplification. Among the four dedifferentiated EC cases, one had POLEmut, one had dMMR, one had p53abn, and one had NSMP with concurrent <italic>HER2</italic> and <italic>NF1</italic> mutations. Additionally, one case of mixed carcinoma (serous and sarcoma) was classified as a dMMR.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>This study confirmed that a single-test, DNA-based NGS panel for molecular classification of EC achieves high concordance with the multi-platform Sanger &#x2b; IHC molecular classification, resolving Sangers low sensitivity for low-frequency POLE mutations. By addressing the discrepancies caused by the lower sensitivity of Sanger sequencing in detecting <italic>POLE</italic> mutations, the consistency between NGS and Sanger &#x2b; IHC classifications improved significantly, with discordance observed in only 2.6% of cases (5 out of 194). These findings confirm and refine prior evidence on NGS-based EC molecular classification and align with previous research by Huvila et al. [<xref ref-type="bibr" rid="B10">10</xref>], who reported a high agreement rate between the ProMisE molecular classification and the FoundationOne NGS panel, and by Li et al. [<xref ref-type="bibr" rid="B11">11</xref>], who found high concordance between an 11-gene NGS panel and the ProMisE classification in 70 cases.</p>
<p>A key practical advancement is resolving limitations in POLE mutation detection: Sanger sequencing fails to identify low-abundance POLE mutations in low-tumor-content samples, risking misclassification of POLEmut as p53abn/MMRd and overtreatment. Currently, there is no validated immunohistochemical surrogate for pathogenic POLE mutations suitable for routine clinical use. This represents one of the main limitations of the WHO molecular classification and poses a challenge in clinical practice. The accuracy of both NGS and Sanger sequencing was significantly affected by the tumor cell content. In this study, NGS detection of genes, such as <italic>POLE,</italic> covered CDS and approximately 20 base pairs of the exon-intron junction, with a mutation frequency &#x3e;1%, capable of detecting key low-frequency pathogenic variants. In contrast, Sanger sequencing can only detect variants with a frequency &#x3e;20% under routine conditions [<xref ref-type="bibr" rid="B19">19</xref>]. Owing to the detection threshold, there is a risk of missing cases that are limited to the uterus, clinically early, with small tumors and low tumor cell proportions. When paraffin tumor samples have less than 20% tumor cell content, it can affect the detection of low-abundance <italic>POLE</italic> gene mutations, such as the seven cases in this study where NGS detected <italic>POLE</italic> gene mutations (V411L or P286R) with mutation frequencies of 7.2%, 6.9%, 5.1%, 4.4%, 4.2%, 4.2%, and 3.2%, respectively, but Sanger sequencing failed to detect these <italic>POLE</italic> variations. After tumor tissue separation and enrichment to ensure that the tumor content was above 20%, Sanger sequencing detected <italic>POLE</italic> mutations in all seven cases. This indicates that when paraffin tumor samples are not quality-controlled for tumor cell content by HE staining, Sanger sequencing may miss some cases of <italic>POLE</italic> mutations, incorrectly classifying POLEmut as the p53abn or MMRd type, leading to erroneous prognostic predictions and overtreatment. Therefore, when the tumor cell content in a sample is low, it is crucial to match the HE-stained cancer foci, perform tumor tissue separation and enrichment, and ensure the highest quality and accuracy of the selected samples, particularly for tumors with low-frequency <italic>POLE</italic> mutations. Therefore, we recommend NGS over Sanger sequencing for detecting <italic>POLE</italic> mutations.</p>
<p>Furthermore, multiple studies have underscored the reliability of NGS in assessing MSI status compared with IHC-based MMR protein detection [<xref ref-type="bibr" rid="B20">20</xref>&#x2013;<xref ref-type="bibr" rid="B22">22</xref>]. Similarly, the concordance between NGS-based <italic>TP53</italic> mutation detection and IHC-based p53 status has been explored, revealing a moderate agreement [<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B23">23</xref>]. These studies highlight the potential of NGS for the clinical molecular classification of EC, offering a streamlined and highly sensitive approach.</p>
<p>In this study, the concordance between NGS-based MSI status and IHC-based MMR status was high (97.9%), with four discordant cases. The three IHC-diagnosed dMMR cases were classified as microsatellite stable (MSS) by NGS, which was attributed to low tumor purity, insufficient MSI loci in the panel, bioinformatic threshold, subclonal MMR deficiency and <italic>MLH1</italic> promoter hypermethylation, a phenomenon previously reported [<xref ref-type="bibr" rid="B24">24</xref>], indicating that <italic>MLH1</italic> methylation can lead to the loss of protein expression without detectable MMR gene mutations. Additionally, two patients with partial loss of MMR protein expression also exhibited <italic>MLH1</italic> promoter hypermethylation, reinforcing the association between epigenetic modifications and MMR deficiency. One case, initially classified as pMMR by IHC, was reclassified as MSI-H based on the NGS analysis. This discordance may be explained by retained immunoreactivity despite loss of protein function, which can occur when amino acid substitutions lead to the functional loss of MMR proteins while maintaining their immunoreactivity. Such cases highlight the limitations of IHC alone in accurately assessing MMR status and underscore the importance of integrating molecular testing for precise classification.</p>
<p>The evaluation of p53 status revealed moderate concordance (kappa &#x3d; 0.688) between NGS-based <italic>TP53</italic> mutation detection and IHC-based p53 expression. The use of a less commonly used antibody clone (MX008) for p53 IHC staining may limit comparability of immunohistochemical results with those from other similar cohorts. Notably, most discrepancies occurred within the POLEmut and MMRd subtypes, where <italic>TP53</italic> mutations are often passenger mutations that do not influence the molecular classification. After excluding these subtypes, the agreement between the NGS and IHC methods improved dramatically to 97.5%, underscoring the importance of sequential molecular classification steps in improving diagnostic precision. The remaining discordant cases warrant further investigation because they pose clinical challenges for treatment decision-making. The emergence of novel <italic>TP53</italic> mutations such as V157_M160del and R202_V203del highlights the need for ongoing research to elucidate their functional and clinical implications.</p>
<p>Notably, this study reinforces the translational value of NGS beyond molecular classification: the detection of mutations associated with targeted therapy trials (including <italic>PTEN, PIK3CA, ARID1A, KRAS, CTNNB1,</italic> and <italic>HER2</italic>) and prognostic markers (including <italic>KRAS</italic> and <italic>CTNNB1</italic>) aligns with emerging precision oncology data. These findings are crucial for guiding targeted therapies and improving prognostic assessment in patients with advanced EC. For instance, mutations in PI3K/AKT pathway genes (e.g., <italic>PIK3CA</italic> and <italic>PTEN</italic>) are actionable targets for inhibitors such as alpelisib and capivasertib [<xref ref-type="bibr" rid="B25">25</xref>&#x2013;<xref ref-type="bibr" rid="B27">27</xref>]. Similarly, <italic>HER2</italic> amplifications identified in p53 abnormal (p53abn) EC subtypes can inform the use of <italic>HER2</italic>-targeted therapies such as trastuzumab, which has been incorporated into clinical guidelines for <italic>HER2</italic>-positive, advanced, or recurrent serous EC [<xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B29">29</xref>]. Additionally, the detection of <italic>BRCA1/2</italic> mutations opens avenues for PARP inhibitor therapy, which has shown efficacy in <italic>BRCA</italic>-mutated cancers [<xref ref-type="bibr" rid="B30">30</xref>, <xref ref-type="bibr" rid="B31">31</xref>].</p>
<p>The NSMP subtype, characterized by its molecular heterogeneity, includes tumors with aggressive features such as <italic>KRAS</italic> and <italic>CTNNB1</italic> mutations. Identifying these mutations within the NSMP group provides valuable prognostic information and underscores the need for further risk stratification. For example, <italic>KRAS</italic> mutations in combination with wild-type <italic>ARID1A</italic> have been associated with poorer disease-free survival [<xref ref-type="bibr" rid="B32">32</xref>], whereas <italic>CTNNB1</italic> mutations are linked to reduced progression-free survival in younger patients with early stage EC [<xref ref-type="bibr" rid="B33">33</xref>]. These insights emphasize the role of comprehensive molecular profiling in refining the prognosis and tailoring individualized treatment strategies.</p>
<p>Despite the clear advantages of NGS-based molecular classification, there are inherent limitations to this approach. The retrospective nature, limited cohort size, and single-center design of this study may affect the generalizability of our findings to a broader population. Additionally, the absence of long-term clinical follow-up and prognostic data necessitates further prospective studies to validate the clinical utility of NGS-based molecular classifications for EC; we cannot confirm NGS-based molecular classifications therapeutic predictive utility.</p>
<p>While this study aligns with and builds on existing literature, we provide actionable, practice-ready refinements: the single-test DNA-based NGS panel is a sensitive, streamlined alternative to Sanger &#x2b; IHC for EC molecular classification and resolves POLE detection failures. Additionally, the NGS panel identified numerous mutations associated with targeted therapy trials, enhanced prognosis, and enabled personalized treatment strategies for patients with advanced EC. These findings support the integration of NGS-based molecular classification into clinical practice to improve the diagnostic precision and treatment outcomes.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s5">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/<xref ref-type="sec" rid="s11">Supplementary Material</xref>.</p>
</sec>
<sec sec-type="ethics-statement" id="s6">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the Ethics Committee of Fujian Provincial Hospital (No. K2024-07-018). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="s7">
<title>Author contributions</title>
<p>LC: Conceptualization, methodology, investigation, data collection, and manuscript drafting. ZY: Formal analysis, validation, data interpretation, and statistical analysis. XY: Methodology development, technical support for next-generation sequencing, and data curation. YW: Supervision, project administration, manuscript review, and funding acquisition. XC: Pathological assessment, immunohistochemistry analysis, and interpretation of results. JL: Conceptualization, supervision, manuscript revision, and final approval of the version to be published. All authors contributed to the article and approved the submitted version.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>The authors thank all personnel involved in the clinical activities for their passionate work.</p>
</ack>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s10">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="supplementary-material" id="s11">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.por-journal.com/articles/10.3389/pore.2026.1612465/full#supplementary-material">https://www.por-journal.com/articles/10.3389/pore.2026.1612465/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material>
<label>SUPPLEMENTARY FIGURE S1</label>
<caption>
<p>Methylation-specific PCR. Representative examples of MSP analysis of MLH1 methylation. (U) Lanes represent amplification of unmethylated alleles, and (M) lanes contain only methylated alleles (Lane M represents a 100-bp DNA marker).</p>
</caption>
</supplementary-material>
<supplementary-material>
<label>SUPPLEMENTARY FIGURE S2</label>
<caption>
<p>Subclonal MMR immunostaining. Immunostaining for MLH1 and PMS2 in cases 50 and 122, respectively, revealed a subclonal staining pattern.</p>
</caption>
</supplementary-material>
<supplementary-material>
<label>SUPPLEMENTARY FIGURE S3</label>
<caption>
<p>Subclonal p53 immunostaining. <bold>(A,B)</bold> Cases showing a combination of normal (wild-type) and p53abn overexpression. <bold>(C,D)</bold> Cases showing a combination of normal (wild-type) and complete absence.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Image3.jpeg" id="SM1" mimetype="application/jpeg" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Supplementaryfile1.docx" id="SM2" mimetype="application/docx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Image1.jpeg" id="SM3" mimetype="application/jpeg" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Image2.jpeg" id="SM4" mimetype="application/jpeg" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<ref-list>
<title>References</title>
<ref id="B1">
<label>1.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Morrison</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Balega</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Buckley</surname>
<given-names>L</given-names>
</name>
<name>
<surname>Clamp</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Crosbie</surname>
<given-names>E</given-names>
</name>
<name>
<surname>Drew</surname>
<given-names>Y</given-names>
</name>
<etal/>
</person-group> <article-title>British gynaecological cancer society (bgcs) uterine cancer guidelines: recommendations for practice</article-title>. <source>Eur Journal Obstetrics, Gynecology, Reproductive Biology</source> (<year>2022</year>) <volume>270</volume>:<fpage>50</fpage>&#x2013;<lpage>89</lpage>. <pub-id pub-id-type="doi">10.1016/j.ejogrb.2021.11.423</pub-id>
<pub-id pub-id-type="pmid">35065448</pub-id>
</mixed-citation>
</ref>
<ref id="B2">
<label>2.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kandoth</surname>
<given-names>C</given-names>
</name>
<name>
<surname>Schultz</surname>
<given-names>N</given-names>
</name>
<name>
<surname>Cherniack</surname>
<given-names>AD</given-names>
</name>
<name>
<surname>Akbani</surname>
<given-names>R</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>Y</given-names>
</name>
<name>
<surname>Shen</surname>
<given-names>H</given-names>
</name>
<etal/>
</person-group> <article-title>Integrated genomic characterization of endometrial carcinoma</article-title>. <source>Nature</source> (<year>2013</year>) <volume>497</volume>:<fpage>67</fpage>&#x2013;<lpage>73</lpage>. <pub-id pub-id-type="doi">10.1038/nature12113</pub-id>
<pub-id pub-id-type="pmid">23636398</pub-id>
</mixed-citation>
</ref>
<ref id="B3">
<label>3.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Stelloo</surname>
<given-names>E</given-names>
</name>
<name>
<surname>Bosse</surname>
<given-names>T</given-names>
</name>
<name>
<surname>Nout</surname>
<given-names>RA</given-names>
</name>
<name>
<surname>MacKay</surname>
<given-names>HJ</given-names>
</name>
<name>
<surname>Church</surname>
<given-names>DN</given-names>
</name>
<name>
<surname>Nijman</surname>
<given-names>HW</given-names>
</name>
<etal/>
</person-group> <article-title>Refining prognosis and identifying targetable pathways for high-risk endometrial cancer; a transportec initiative</article-title>. <source>Mod Pathol</source> (<year>2015</year>) <volume>28</volume>:<fpage>836</fpage>&#x2013;<lpage>44</lpage>. <pub-id pub-id-type="doi">10.1038/modpathol.2015.43</pub-id>
<pub-id pub-id-type="pmid">25720322</pub-id>
</mixed-citation>
</ref>
<ref id="B4">
<label>4.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kommoss</surname>
<given-names>S</given-names>
</name>
<name>
<surname>McConechy</surname>
<given-names>MK</given-names>
</name>
<name>
<surname>Kommoss</surname>
<given-names>F</given-names>
</name>
<name>
<surname>Leung</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Bunz</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Magrill</surname>
<given-names>J</given-names>
</name>
<etal/>
</person-group> <article-title>Final validation of the promise molecular classifier for endometrial carcinoma in a large population-based case series</article-title>. <source>Ann Oncol</source> (<year>2018</year>) <volume>29</volume>:<fpage>1180</fpage>&#x2013;<lpage>8</lpage>. <pub-id pub-id-type="doi">10.1093/annonc/mdy058</pub-id>
<pub-id pub-id-type="pmid">29432521</pub-id>
</mixed-citation>
</ref>
<ref id="B5">
<label>5.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Cree</surname>
<given-names>IA</given-names>
</name>
<name>
<surname>White</surname>
<given-names>VA</given-names>
</name>
<name>
<surname>Indave</surname>
<given-names>BI</given-names>
</name>
<name>
<surname>Lokuhetty</surname>
<given-names>D</given-names>
</name>
</person-group>. <article-title>Revising the who classification: female genital tract tumours</article-title>. <source>Histopathology</source> (<year>2020</year>) <volume>76</volume>:<fpage>151</fpage>&#x2013;<lpage>6</lpage>. <pub-id pub-id-type="doi">10.1111/his.13977</pub-id>
<pub-id pub-id-type="pmid">31846528</pub-id>
</mixed-citation>
</ref>
<ref id="B6">
<label>6.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Grosse</surname>
<given-names>S</given-names>
</name>
<name>
<surname>El Hajj</surname>
<given-names>H</given-names>
</name>
<name>
<surname>Genestie</surname>
<given-names>C</given-names>
</name>
<name>
<surname>Morice</surname>
<given-names>P</given-names>
</name>
<name>
<surname>Gouy</surname>
<given-names>S</given-names>
</name>
</person-group>. <article-title>Streamlined approach to endometrial cancer: figo 2023 staging</article-title>. <source>Int J Gynecol Cancer</source> (<year>2024</year>) <volume>35</volume>:<fpage>101839</fpage>. <pub-id pub-id-type="doi">10.1136/ijgc-2024-006160</pub-id>
<pub-id pub-id-type="pmid">39438069</pub-id>
</mixed-citation>
</ref>
<ref id="B7">
<label>7.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Le&#xf3;n-Castillo</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Gilvazquez</surname>
<given-names>E</given-names>
</name>
<name>
<surname>Nout</surname>
<given-names>R</given-names>
</name>
<name>
<surname>Smit</surname>
<given-names>VT</given-names>
</name>
<name>
<surname>McAlpine</surname>
<given-names>JN</given-names>
</name>
<name>
<surname>McConechy</surname>
<given-names>M</given-names>
</name>
<etal/>
</person-group> <article-title>Clinicopathological and molecular characterisation of &#x27;Multiple-Classifier&#x27; endometrial carcinomas</article-title>. <source>J Pathol</source> (<year>2020</year>) <volume>250</volume>:<fpage>312</fpage>&#x2013;<lpage>22</lpage>. <pub-id pub-id-type="doi">10.1002/path.5373</pub-id>
<pub-id pub-id-type="pmid">31829447</pub-id>
</mixed-citation>
</ref>
<ref id="B8">
<label>8.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kang</surname>
<given-names>N</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>X</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>Z</given-names>
</name>
<name>
<surname>Dai</surname>
<given-names>Y</given-names>
</name>
<name>
<surname>Lu</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Su</surname>
<given-names>W</given-names>
</name>
<etal/>
</person-group> <article-title>Validation of a one-step genomics-based molecular classifier for endometrial carcinoma in a large Chinese population</article-title>. <source>Pathol Res Pract</source> (<year>2024</year>) <volume>254</volume>:<fpage>155152</fpage>. <pub-id pub-id-type="doi">10.1016/j.prp.2024.155152</pub-id>
<pub-id pub-id-type="pmid">38277742</pub-id>
</mixed-citation>
</ref>
<ref id="B9">
<label>9.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Moroney</surname>
<given-names>MR</given-names>
</name>
<name>
<surname>Davies</surname>
<given-names>KD</given-names>
</name>
<name>
<surname>Wilberger</surname>
<given-names>AC</given-names>
</name>
<name>
<surname>Sheeder</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Post</surname>
<given-names>MD</given-names>
</name>
<name>
<surname>Berning</surname>
<given-names>AA</given-names>
</name>
<etal/>
</person-group> <article-title>Molecular markers in recurrent stage I, grade 1 endometrioid endometrial cancers</article-title>. <source>Gynecol Oncol</source> (<year>2019</year>) <volume>153</volume>:<fpage>517</fpage>&#x2013;<lpage>20</lpage>. <pub-id pub-id-type="doi">10.1016/j.ygyno.2019.03.100</pub-id>
<pub-id pub-id-type="pmid">30910249</pub-id>
</mixed-citation>
</ref>
<ref id="B10">
<label>10.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Huvila</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Orte</surname>
<given-names>K</given-names>
</name>
<name>
<surname>Vainio</surname>
<given-names>P</given-names>
</name>
<name>
<surname>Mett&#xe4;l&#xe4;</surname>
<given-names>T</given-names>
</name>
<name>
<surname>Joutsiniemi</surname>
<given-names>T</given-names>
</name>
<name>
<surname>Hietanen</surname>
<given-names>S</given-names>
</name>
</person-group>. <article-title>Molecular subtype diagnosis of endometrial carcinoma: comparison of the next-generation sequencing panel and proactive molecular risk classifier for endometrial cancer classifier</article-title>. <source>Hum Pathol</source> (<year>2021</year>) <volume>111</volume>:<fpage>98</fpage>&#x2013;<lpage>109</lpage>. <pub-id pub-id-type="doi">10.1016/j.humpath.2021.02.006</pub-id>
<pub-id pub-id-type="pmid">33662372</pub-id>
</mixed-citation>
</ref>
<ref id="B11">
<label>11.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Li</surname>
<given-names>L</given-names>
</name>
<name>
<surname>Chen</surname>
<given-names>F</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Zhu</surname>
<given-names>W</given-names>
</name>
<name>
<surname>Lin</surname>
<given-names>L</given-names>
</name>
<name>
<surname>Chen</surname>
<given-names>L</given-names>
</name>
<etal/>
</person-group> <article-title>Molecular classification grade 3 endometrial endometrioid carcinoma using a next-generation sequencing-based gene panel</article-title>. <source>Front Oncol</source> (<year>2022</year>) <volume>12</volume>:<fpage>935694</fpage>. <pub-id pub-id-type="doi">10.3389/fonc.2022.935694</pub-id>
<pub-id pub-id-type="pmid">36003784</pub-id>
</mixed-citation>
</ref>
<ref id="B12">
<label>12.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Slatko</surname>
<given-names>BE</given-names>
</name>
<name>
<surname>Gardner</surname>
<given-names>AF</given-names>
</name>
<name>
<surname>Ausubel</surname>
<given-names>FM</given-names>
</name>
</person-group>. <article-title>Overview of next-generation sequencing technologies</article-title>. <source>Curr Protoc Mol Biol</source> (<year>2018</year>) <volume>122</volume>:<fpage>e59</fpage>. <pub-id pub-id-type="doi">10.1002/cpmb.59</pub-id>
<pub-id pub-id-type="pmid">29851291</pub-id>
</mixed-citation>
</ref>
<ref id="B13">
<label>13.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Singh</surname>
<given-names>N</given-names>
</name>
<name>
<surname>Piskorz</surname>
<given-names>AM</given-names>
</name>
<name>
<surname>Bosse</surname>
<given-names>T</given-names>
</name>
<name>
<surname>Jimenez-Linan</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Rous</surname>
<given-names>B</given-names>
</name>
<name>
<surname>Brenton</surname>
<given-names>JD</given-names>
</name>
<etal/>
</person-group> <article-title>P53 immunohistochemistry is an accurate surrogate for Tp53 mutational analysis in endometrial carcinoma biopsies</article-title>. <source>J Pathol</source> (<year>2020</year>) <volume>250</volume>:<fpage>336</fpage>&#x2013;<lpage>45</lpage>. <pub-id pub-id-type="doi">10.1002/path.5375</pub-id>
<pub-id pub-id-type="pmid">31829441</pub-id>
</mixed-citation>
</ref>
<ref id="B14">
<label>14.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>K&#xf6;bel</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Ronnett</surname>
<given-names>BM</given-names>
</name>
<name>
<surname>Singh</surname>
<given-names>N</given-names>
</name>
<name>
<surname>Soslow</surname>
<given-names>RA</given-names>
</name>
<name>
<surname>Gilks</surname>
<given-names>CB</given-names>
</name>
<name>
<surname>McCluggage</surname>
<given-names>WG</given-names>
</name>
</person-group>. <article-title>Interpretation of P53 immunohistochemistry in endometrial carcinomas: toward increased reproducibility</article-title>. <source>Int J Gynecol Pathol</source> (<year>2019</year>) <volume>38</volume>(<issue>Suppl. 1</issue>):<fpage>S123</fpage>&#x2013;<lpage>s31</lpage>. <pub-id pub-id-type="doi">10.1097/pgp.0000000000000488</pub-id>
<pub-id pub-id-type="pmid">29517499</pub-id>
</mixed-citation>
</ref>
<ref id="B15">
<label>15.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>McConechy</surname>
<given-names>MK</given-names>
</name>
<name>
<surname>Talhouk</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Leung</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Chiu</surname>
<given-names>D</given-names>
</name>
<name>
<surname>Yang</surname>
<given-names>W</given-names>
</name>
<name>
<surname>Senz</surname>
<given-names>J</given-names>
</name>
<etal/>
</person-group> <article-title>Endometrial carcinomas with pole exonuclease domain mutations have a favorable prognosis</article-title>. <source>Clin Cancer Res</source> (<year>2016</year>) <volume>22</volume>:<fpage>2865</fpage>&#x2013;<lpage>73</lpage>. <pub-id pub-id-type="doi">10.1158/1078-0432.Ccr-15-2233</pub-id>
<pub-id pub-id-type="pmid">26763250</pub-id>
</mixed-citation>
</ref>
<ref id="B16">
<label>16.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Le&#xf3;n-Castillo</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Britton</surname>
<given-names>H</given-names>
</name>
<name>
<surname>McConechy</surname>
<given-names>MK</given-names>
</name>
<name>
<surname>McAlpine</surname>
<given-names>JN</given-names>
</name>
<name>
<surname>Nout</surname>
<given-names>R</given-names>
</name>
<name>
<surname>Kommoss</surname>
<given-names>S</given-names>
</name>
<etal/>
</person-group> <article-title>Interpretation of somatic pole mutations in endometrial carcinoma</article-title>. <source>J Pathol</source> (<year>2020</year>) <volume>250</volume>:<fpage>323</fpage>&#x2013;<lpage>35</lpage>. <pub-id pub-id-type="doi">10.1002/path.5372</pub-id>
<pub-id pub-id-type="pmid">31829442</pub-id>
</mixed-citation>
</ref>
<ref id="B17">
<label>17.</label>
<mixed-citation publication-type="book">
<person-group person-group-type="author">
<name>
<surname>Chakravarty</surname>
<given-names>D</given-names>
</name>
<name>
<surname>Gao</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Phillips</surname>
<given-names>SM</given-names>
</name>
<name>
<surname>Kundra</surname>
<given-names>R</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>H</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>J</given-names>
</name>
<etal/>
</person-group> <article-title>Oncokb: A Precision Oncology Knowledge Base</article-title>. <source>JCO Precision Oncol</source>. (<year>2017</year>). p. <fpage>2017</fpage>. <pub-id pub-id-type="doi">10.1200/po.17.00011</pub-id>
</mixed-citation>
</ref>
<ref id="B18">
<label>18.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Li</surname>
<given-names>MM</given-names>
</name>
<name>
<surname>Datto</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Duncavage</surname>
<given-names>EJ</given-names>
</name>
<name>
<surname>Kulkarni</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Lindeman</surname>
<given-names>NI</given-names>
</name>
<name>
<surname>Roy</surname>
<given-names>S</given-names>
</name>
<etal/>
</person-group> <article-title>Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the association for molecular pathology, American society of clinical oncology, and college of American pathologists</article-title>. <source>J Mol Diagn</source> (<year>2017</year>) <volume>19</volume>:<fpage>4</fpage>&#x2013;<lpage>23</lpage>. <pub-id pub-id-type="doi">10.1016/j.jmoldx.2016.10.002</pub-id>
<pub-id pub-id-type="pmid">27993330</pub-id>
</mixed-citation>
</ref>
<ref id="B19">
<label>19.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Rohlin</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Wernersson</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Engwall</surname>
<given-names>Y</given-names>
</name>
<name>
<surname>Wiklund</surname>
<given-names>L</given-names>
</name>
<name>
<surname>Bj&#xf6;rk</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Nordling</surname>
<given-names>M</given-names>
</name>
</person-group>. <article-title>Parallel sequencing used in detection of mosaic mutations: Comparison with four diagnostic DNA screening techniques</article-title>. <source>Hum Mutat</source> (<year>2009</year>) <volume>30</volume>:<fpage>1012</fpage>&#x2013;<lpage>20</lpage>. <pub-id pub-id-type="doi">10.1002/humu.20980</pub-id>
<pub-id pub-id-type="pmid">19347965</pub-id>
</mixed-citation>
</ref>
<ref id="B20">
<label>20.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>McConechy</surname>
<given-names>MK</given-names>
</name>
<name>
<surname>Talhouk</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Li-Chang</surname>
<given-names>HH</given-names>
</name>
<name>
<surname>Leung</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Huntsman</surname>
<given-names>DG</given-names>
</name>
<name>
<surname>Gilks</surname>
<given-names>CB</given-names>
</name>
<etal/>
</person-group> <article-title>Detection of DNA mismatch repair (mmr) deficiencies by immunohistochemistry can effectively diagnose the microsatellite instability (msi) phenotype in endometrial carcinomas</article-title>. <source>Gynecol Oncol</source> (<year>2015</year>) <volume>137</volume>:<fpage>306</fpage>&#x2013;<lpage>10</lpage>. <pub-id pub-id-type="doi">10.1016/j.ygyno.2015.01.541</pub-id>
<pub-id pub-id-type="pmid">25636458</pub-id>
</mixed-citation>
</ref>
<ref id="B21">
<label>21.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Streel</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Salmon</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Dheur</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Bours</surname>
<given-names>V</given-names>
</name>
<name>
<surname>Leroi</surname>
<given-names>N</given-names>
</name>
<name>
<surname>Habran</surname>
<given-names>L</given-names>
</name>
<etal/>
</person-group> <article-title>Diagnostic performance of immunohistochemistry compared to molecular techniques for microsatellite instability and P53 mutation detection in endometrial cancer</article-title>. <source>Int J Mol Sci</source> (<year>2023</year>) <volume>24</volume>:<fpage>24</fpage>. <pub-id pub-id-type="doi">10.3390/ijms24054866</pub-id>
<pub-id pub-id-type="pmid">36902292</pub-id>
</mixed-citation>
</ref>
<ref id="B22">
<label>22.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Dedeurwaerdere</surname>
<given-names>F</given-names>
</name>
<name>
<surname>Claes</surname>
<given-names>KB</given-names>
</name>
<name>
<surname>Van Dorpe</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Rottiers</surname>
<given-names>I</given-names>
</name>
<name>
<surname>Van der Meulen</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Breyne</surname>
<given-names>J</given-names>
</name>
<etal/>
</person-group> <article-title>Comparison of microsatellite instability detection by immunohistochemistry and molecular techniques in colorectal and endometrial cancer</article-title>. <source>Sci Rep</source> (<year>2021</year>) <volume>11</volume>:<fpage>12880</fpage>. <pub-id pub-id-type="doi">10.1038/s41598-021-91974-x</pub-id>
<pub-id pub-id-type="pmid">34145315</pub-id>
</mixed-citation>
</ref>
<ref id="B23">
<label>23.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Vermij</surname>
<given-names>L</given-names>
</name>
<name>
<surname>L&#xe9;on-Castillo</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Singh</surname>
<given-names>N</given-names>
</name>
<name>
<surname>Powell</surname>
<given-names>ME</given-names>
</name>
<name>
<surname>Edmondson</surname>
<given-names>RJ</given-names>
</name>
<name>
<surname>Genestie</surname>
<given-names>C</given-names>
</name>
<etal/>
</person-group> <article-title>P53 immunohistochemistry in endometrial cancer: clinical and molecular correlates in the Portec-3 trial</article-title>. <source>Mod Pathol</source> (<year>2022</year>) <volume>35</volume>:<fpage>1475</fpage>&#x2013;<lpage>83</lpage>. <pub-id pub-id-type="doi">10.1038/s41379-022-01102-x</pub-id>
<pub-id pub-id-type="pmid">35752743</pub-id>
</mixed-citation>
</ref>
<ref id="B24">
<label>24.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Stelloo</surname>
<given-names>E</given-names>
</name>
<name>
<surname>Jansen</surname>
<given-names>AML</given-names>
</name>
<name>
<surname>Osse</surname>
<given-names>EM</given-names>
</name>
<name>
<surname>Nout</surname>
<given-names>RA</given-names>
</name>
<name>
<surname>Creutzberg</surname>
<given-names>CL</given-names>
</name>
<name>
<surname>Ruano</surname>
<given-names>D</given-names>
</name>
<etal/>
</person-group> <article-title>Practical guidance for mismatch repair-deficiency testing in endometrial cancer</article-title>. <source>Ann Oncol</source> (<year>2017</year>) <volume>28</volume>:<fpage>96</fpage>&#x2013;<lpage>102</lpage>. <pub-id pub-id-type="doi">10.1093/annonc/mdw542</pub-id>
<pub-id pub-id-type="pmid">27742654</pub-id>
</mixed-citation>
</ref>
<ref id="B25">
<label>25.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Juric</surname>
<given-names>D</given-names>
</name>
<name>
<surname>Rodon</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Tabernero</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Janku</surname>
<given-names>F</given-names>
</name>
<name>
<surname>Burris</surname>
<given-names>HA</given-names>
</name>
<name>
<surname>Schellens</surname>
<given-names>JHM</given-names>
</name>
<etal/>
</person-group> <article-title>Phosphatidylinositol 3-Kinase &#x391;-Selective inhibition with alpelisib (Byl719) in Pik3ca-Altered solid tumors: results from the first-in-human study</article-title>. <source>J Clin Oncol</source> (<year>2018</year>) <volume>36</volume>:<fpage>1291</fpage>&#x2013;<lpage>9</lpage>. <pub-id pub-id-type="doi">10.1200/jco.2017.72.7107</pub-id>
<pub-id pub-id-type="pmid">29401002</pub-id>
</mixed-citation>
</ref>
<ref id="B26">
<label>26.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Rubinstein</surname>
<given-names>MM</given-names>
</name>
<name>
<surname>Hyman</surname>
<given-names>DM</given-names>
</name>
<name>
<surname>Caird</surname>
<given-names>I</given-names>
</name>
<name>
<surname>Won</surname>
<given-names>H</given-names>
</name>
<name>
<surname>Soldan</surname>
<given-names>K</given-names>
</name>
<name>
<surname>Seier</surname>
<given-names>K</given-names>
</name>
<etal/>
</person-group> <article-title>Phase 2 study of Ly3023414 in patients with advanced endometrial cancer harboring activating mutations in the Pi3k pathway</article-title>. <source>Cancer</source> (<year>2020</year>) <volume>126</volume>:<fpage>1274</fpage>&#x2013;<lpage>82</lpage>. <pub-id pub-id-type="doi">10.1002/cncr.32677</pub-id>
<pub-id pub-id-type="pmid">31880826</pub-id>
</mixed-citation>
</ref>
<ref id="B27">
<label>27.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Voss</surname>
<given-names>MH</given-names>
</name>
<name>
<surname>Gordon</surname>
<given-names>MS</given-names>
</name>
<name>
<surname>Mita</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Rini</surname>
<given-names>B</given-names>
</name>
<name>
<surname>Makker</surname>
<given-names>V</given-names>
</name>
<name>
<surname>Macarulla</surname>
<given-names>T</given-names>
</name>
<etal/>
</person-group> <article-title>Phase 1 study of Mtorc1/2 inhibitor sapanisertib (Tak-228) in advanced solid tumours, with an expansion phase in renal, endometrial or bladder cancer</article-title>. <source>Br J Cancer</source> (<year>2020</year>) <volume>123</volume>:<fpage>1590</fpage>&#x2013;<lpage>8</lpage>. <pub-id pub-id-type="doi">10.1038/s41416-020-01041-x</pub-id>
<pub-id pub-id-type="pmid">32913286</pub-id>
</mixed-citation>
</ref>
<ref id="B28">
<label>28.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Koh</surname>
<given-names>WJ</given-names>
</name>
<name>
<surname>Abu-Rustum</surname>
<given-names>NR</given-names>
</name>
<name>
<surname>Bean</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Bradley</surname>
<given-names>K</given-names>
</name>
<name>
<surname>Campos</surname>
<given-names>SM</given-names>
</name>
<name>
<surname>Cho</surname>
<given-names>KR</given-names>
</name>
<etal/>
</person-group> <article-title>Uterine neoplasms, version 1.2018, nccn Clinical practice guidelines in oncology</article-title>. <source>J Natl Compr Canc Netw</source> (<year>2018</year>) <volume>16</volume>:<fpage>170</fpage>&#x2013;<lpage>99</lpage>. <pub-id pub-id-type="doi">10.6004/jnccn.2018.0006</pub-id>
<pub-id pub-id-type="pmid">29439178</pub-id>
</mixed-citation>
</ref>
<ref id="B29">
<label>29.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Erickson</surname>
<given-names>BK</given-names>
</name>
<name>
<surname>Zeybek</surname>
<given-names>B</given-names>
</name>
<name>
<surname>Santin</surname>
<given-names>AD</given-names>
</name>
<name>
<surname>Fader</surname>
<given-names>AN</given-names>
</name>
</person-group>. <article-title>Targeting human epidermal growth factor receptor 2 (Her2) in gynecologic malignancies</article-title>. <source>Curr Opin Obstet Gynecol</source> (<year>2020</year>) <volume>32</volume>:<fpage>57</fpage>&#x2013;<lpage>64</lpage>. <pub-id pub-id-type="doi">10.1097/gco.0000000000000599</pub-id>
<pub-id pub-id-type="pmid">31833974</pub-id>
</mixed-citation>
</ref>
<ref id="B30">
<label>30.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Weiss</surname>
<given-names>AS</given-names>
</name>
<name>
<surname>Swisher</surname>
<given-names>E</given-names>
</name>
<name>
<surname>Pennington</surname>
<given-names>KP</given-names>
</name>
<name>
<surname>Radke</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Khasnavis</surname>
<given-names>N</given-names>
</name>
<name>
<surname>Garcia</surname>
<given-names>RL</given-names>
</name>
<etal/>
</person-group> <article-title>Inherited mutations in fallopian tube, ovarian and primary peritoneal carcinoma: changes in diagnoses and mutational frequency over 20 years</article-title>. <source>Gynecol Oncol</source> (<year>2020</year>) <volume>159</volume>:<fpage>214</fpage>&#x2013;<lpage>20</lpage>. <pub-id pub-id-type="doi">10.1016/j.ygyno.2020.06.509</pub-id>
<pub-id pub-id-type="pmid">32709535</pub-id>
</mixed-citation>
</ref>
<ref id="B31">
<label>31.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Auguste</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Genestie</surname>
<given-names>C</given-names>
</name>
<name>
<surname>De Bruyn</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Adam</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Le Formal</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Drusch</surname>
<given-names>F</given-names>
</name>
<etal/>
</person-group> <article-title>Refinement of high-risk endometrial cancer classification using DNA damage response biomarkers: a transportec initiative</article-title>. <source>Mod Pathol</source> (<year>2018</year>) <volume>31</volume>:<fpage>1851</fpage>&#x2013;<lpage>61</lpage>. <pub-id pub-id-type="doi">10.1038/s41379-018-0055-1</pub-id>
<pub-id pub-id-type="pmid">29955143</pub-id>
</mixed-citation>
</ref>
<ref id="B32">
<label>32.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Asami</surname>
<given-names>Y</given-names>
</name>
<name>
<surname>Kobayashi Kato</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Hiranuma</surname>
<given-names>K</given-names>
</name>
<name>
<surname>Matsuda</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Shimada</surname>
<given-names>Y</given-names>
</name>
<name>
<surname>Ishikawa</surname>
<given-names>M</given-names>
</name>
<etal/>
</person-group> <article-title>Utility of molecular subtypes and genetic alterations for evaluating clinical outcomes in 1029 patients with endometrial cancer</article-title>. <source>Br J Cancer</source> (<year>2023</year>) <volume>128</volume>:<fpage>1582</fpage>&#x2013;<lpage>91</lpage>. <pub-id pub-id-type="doi">10.1038/s41416-023-02203-3</pub-id>
<pub-id pub-id-type="pmid">36797358</pub-id>
</mixed-citation>
</ref>
<ref id="B33">
<label>33.</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kurnit</surname>
<given-names>KC</given-names>
</name>
<name>
<surname>Kim</surname>
<given-names>GN</given-names>
</name>
<name>
<surname>Fellman</surname>
<given-names>BM</given-names>
</name>
<name>
<surname>Urbauer</surname>
<given-names>DL</given-names>
</name>
<name>
<surname>Mills</surname>
<given-names>GB</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>W</given-names>
</name>
<etal/>
</person-group> <article-title>Ctnnb1 (Beta-Catenin) mutation identifies low grade, early stage endometrial cancer patients at increased risk of recurrence</article-title>. <source>Mod Pathol</source> (<year>2017</year>) <volume>30</volume>:<fpage>1032</fpage>&#x2013;<lpage>41</lpage>. <pub-id pub-id-type="doi">10.1038/modpathol.2017.15</pub-id>
<pub-id pub-id-type="pmid">28281553</pub-id>
</mixed-citation>
</ref>
</ref-list>
<fn-group>
<fn fn-type="custom" custom-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1099044/overview">Natalia Buza</ext-link>, Yale University, United States</p>
</fn>
</fn-group>
</back>
</article>