Molecular classification improves preoperative risk assessment of endometrial cancer.

OBJECTIVE: We aimed to evaluate the performance of endometrial cancer (EC) molecular classification in predicting extrauterine disease after primary surgery alone and in combination with other clinical data available in preoperative setting. METHODS: Retrospective single-center observational study including patients with endometrial adenocarcinoma treated with primary surgery between December 1994 and May 2022. Molecular profiling was performed using immunohistochemistry of p53, MLH1, PMS2, MSH2 and MSH6; and KASP genotyping of the 6 most common mutations of POLE gene. Clinical, pathological and imaging information was reviewed. Logistic regression, regression trees and random forest classification techniques (CART) were performed. RESULTS: We enrolled 658 patients, 47 with POLEmut (7.1%), 234 with MMRd (35.6%), 95 with p53abn (14.4%) and 282 with NSMP (42.8%) tumors. Advanced stage after primary surgery (III-IV FIGO 2009) was diagnosed in 11.7% of patients, p53abn tumors showed increased extrauterine spread (34.1%) and nodal involvement (30.1%) (p < .001). In multivariate analysis, only p53abn subgroup (aOR = 16.0, CI95% = 1.5-165.1) and radiological suspicion of extrauterine disease (aOR = 24.2, CI95% = 12.2-48.2) independently predicted the finding of extrauterine disease after primary surgery. In patients with preoperative uterine-confined disease, deep myometrial and cervical involvement in radiological assessment and p53abn molecular subtype were the best variables to identify patients at-risk of occult extrauterine disease after the staging surgery. CONCLUSION: EC molecular classification is more accurate than histotype or grade in preoperative biopsy to predict advanced disease, and together with imaging tests are the most reliable preoperative information. This work provides an initial framework for using molecular information preoperatively to tailor surgical treatment.

The impact of estrogen receptor and L1 cell adhesion molecule expression on endometrial cancer outcome correlates with clinicopathological risk group and molecular subgroup.

OBJECTIVE: To assess the risk stratification of clinicopathologically and molecularly classified endometrial cancer based on estrogen receptor (ER) and L1 cell adhesion molecule (L1CAM) expression. METHODS: This was a retrospective study of patients who underwent primary treatment at a single tertiary center. Carcinomas were classified into 5 clinicopathological risk groups, as per European guidelines. Immunohistochemistry and polymerase-ϵ sequencing were conducted for molecular classification and determination of ER and L1CAM expression. RESULTS: Data from 1044 patients were analyzed. The median follow-up was 67.5 months. In univariable analyses, ER expression correlated with improved disease-specific survival (DSS) in the "no specific molecular profile" (NSMP) (P < 0.001) and mismatch repair deficient (MMRd) (P = 0.002) subgroups. Negative L1CAM expression was associated with enhanced DSS in the NSMP subgroup alone (P < 0.001). ER (hazard ratio [HR] 0.18), but not L1CAM, exhibited prognostic significance within NSMP when controlling for parameters available at the time of diagnosis (tumor histotype, grade, age). ER and L1CAM were not independently associated with DSS within NSMP when controlling for parameters available after surgery (clinicopathological risk groups, age, adjuvant therapy). However, in high-risk-advanced-metastatic cases, both ER (HR 0.26) and L1CAM (HR 3.9) independently correlated with DSS. Similarly, within MMRd, ER was associated with improved DSS in high-risk-advanced-metastatic carcinomas (HR 0.42). CONCLUSION: The prognostic significance of ER and L1CAM varies across clinicopathological risk groups and molecular subgroups of endometrial cancer. Notably, risk assessment for high-risk-advanced-metastatic NSMP and MMRd subtype carcinomas can be refined by ER status.

Uterine cancer restaging according to the updated 2023 FIGO Guidelines does not uniformly affect prognosis: An institutional retrospective cohort study.

OBJECTIVE: Efforts have been made to better risk stratify patients given the rise in incidence of endometrial cancer (EC). The 2023 FIGO staging now incorporates histologic subtype and molecular classification into determination of EC stage. We sought to elucidate if the new staging system demonstrated prognostic differences compared to the 2009 staging system. METHODS: A retrospective chart review was performed on women treated for EC at our institution from September 2013 to May 2023 and combined with the publicly available TCGA Nature 2013 dataset. Detailed clinical information was captured. Patients were restaged according to the 2023 guidelines. Survival estimates were obtained using Kaplan-Meier method, and the log-rank test was used to compare survival curves for progression-free survival (PFS). RESULTS: 919 patients were included in our analysis. The datasets were comparable regarding histologic grade, stage, and age at diagnosis. 175 (31.5%) of patients in the institution dataset and 115 (31.6%) patients in the TCGA dataset experienced a stage change. Most patients whose stage changed were upstaged (275/290; 94.8%). 3-year PFS estimates for stage IA patients with no stage change versus those upstaged were 92.3% (95% CI: 87.2, 95.4) v. 72.0% (95% CI: 68.4, 84.9), p = 0.002. No significant differences in survival difference were seen in other stage subsets. CONCLUSION: Modest survival differences exist in patients with EC originally staged as IA who underwent upstaging. No significant survival difference is observed in patients who are restaged to stage II or III subsets. Improved risk stratification is needed in assessing prognosis and adjuvant therapy for patients with endometrial cancer.

Is It Possible to Predict Tumor Progression Through Genomic Characterization of Monoclonal Gammopathy and Smoldering Multiple Myeloma?

The study of monoclonal serum proteins has led to the generation of two major theories: one proposing that individuals who had monoclonal proteins without any symptoms or evidence of end-organ damage have a benign condition, the other one suggesting that some individuals with asymptomatic monoclonal proteins may progress to multiple myeloma and thus are affected by a monoclonal gammopathy of undetermined significance (MGUS). Longitudinal studies of subjects with MGUS have supported the second theory. Subsequent studies have characterized and defined the existence of another precursor of multiple myeloma, smoldering multiple myeloma (SMM), intermediate between MGUS and multiple myeloma. Primary molecular events, chromosome translocations, and chromosome number alterations resulting in hyperploidy, required for multiple myeloma development, are already observed in myeloma precursors. MGUS and SMM are heterogeneous conditions with the presence of tumors with distinct pathogenic phenotypes and clinical outcomes. The identification of MGUS and SMM patients with a molecularly defined high risk of progression to MM offers the unique opportunity of early intervention with a therapeutic approach on a low tumor burden.

Molecular Classification of Breast Cancer Using Weakly Supervised Learning.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusions: The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Brain tumor-associated epilepsies in adulthood: Current state of diagnostic and individual treatment options.

Brain tumors are one of the most frequent causes of structural epilepsy and set a major burden on treatment costs and the social integrity of patients. Although promising oncological treatment strategies are already available, epileptological treatment is often intractable and requires lifelong epileptological care. Therefore, treatment strategies must be adapted to age-related needs, and specific aspects of late-onset epilepsy (LOE) must be considered. The practical implementation of individual decisions from tumor boards and the current state of the art in scientific knowledge about pathological mechanisms, modern diagnostic procedures and biomarkers, and patient-individualized treatment options into practical epileptological disease management is a prerequisite. This narrative review focuses on the current work progress regarding pathogenesis, diagnosis, and therapy. Exemplarily, interdisciplinary approaches for optimized individualized therapy will be discussed, emphasizing the combination of neurological-epileptological and oncological perspectives.

Identification of prognostic biomarkers for cholangiocarcinoma by combined analysis of molecular characteristics of clinical MVI subtypes and molecular subtypes.

Cholangiocarcinoma (CCA) is widely noted for its high degree of malignancy, rapid progression, and limited therapeutic options. This study was carried out on transcriptome data of 417 CCA samples from different anatomical locations. The effects of lipid metabolism related genes and immune related genes as CCA classifiers were compared. Key genes were derived from MVI subtypes and better molecular subtypes. Pathways such as epithelial mesenchymal transition (EMT) and cell cycle were significantly activated in MVI-positive group. CCA patients were classified into three (four) subtypes based on lipid metabolism (immune) related genes, with better prognosis observed in lipid metabolism-C1, immune-C2, and immune-C4. IPTW analysis found that the prognosis of lipid metabolism-C1 was significantly better than that of lipid metabolism-C2 + C3 before and after correction. KRT16 was finally selected as the key gene. And knockdown of KRT16 inhibited proliferation, migration and invasion of CCA cells.

Molecular classification of metastatic and recurrent endometrial endometrioid carcinoma: prognostic relevance among low- and high-stage tumours.

AIMS: Molecular classification according to The Cancer Genome Atlas (TCGA) improves endometrial endometrioid carcinoma (EEC) prognostication and has specific treatment implications; however, original data were skewed towards low-grade and low-stage tumours. Herein, we molecularly classify EECs metastatic at the time of diagnosis or with subsequently documented recurrent/metastatic disease to examine correlation with clinical outcomes. METHODS: TCGA categories include POLE-mutated, microsatellite instability (MSI), p53 abnormal (p53 abnl) and no specific molecular profile (NSMP). POLE targeted sequencing at exons 9, 11, 13 and 14 and immunohistochemistry (IHC) for PMS2, MSH6 and p53 were performed to establish molecular classification. RESULTS: The distribution in our cohort of 141 EECs was similar to that generally reported in EEC, with nine POLE-mutated (6%), 45 MSI (32%), 16 p53 abnl (11%) and 71 NSMP (50%), with similar distributions between low- and high-stage cohorts. We demonstrate that when stratified by molecular subtype, disease-specific survival from the time of high-stage (stages III-IV) presentation or time of recurrence in low-stage (stages I-II) disease among metastatic and/or recurrent EEC is strongly associated with TCGA classification (high-stage P = 0.02, low-stage P = 0.017). Discordant molecular classification between primary and metastatic/recurrent tumours occurred in four of 105 (3.8%) patients, two related to PMS2/MSH6 IHC and two related to p53 IHC. CONCLUSIONS: We demonstrate that molecular classification is prognostically relevant not only at the time of diagnosis, but also at the time of recurrence and in the metastatic setting. Rare subclonal alterations occur and suggest a role for confirming TCGA classification in recurrent/metastatic tumours.

Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms.

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

Application of novel algorithm on a retrospective series to implement the molecular classification for endometrial cancer.

INTRODUCTION: The study aimed to validate the Betella algorithm, focusing on molecular analyses exclusively for endometrial cancer patients, where molecular classification alters risk assessment based on ESGO/ESTRO/ESP 2020 guidelines. MATERIALS AND METHODS: Conducted between March 2021 and March 2023, the retrospective research involved endometrial cancer patients undergoing surgery and comprehensive molecular analyses. These included p53 and mismatch repair proteins immunohistochemistry, as well as DNA sequencing for POLE exonuclease domain. We applied the Betella algorithm to our population and evaluated the proportion of patients in which the molecular analysis changed the risk class attribution. RESULTS: Out of 102 patients, 97 % obtained complete molecular analyses. The cohort exhibited varying molecular classifications: 10.1 % as POLE ultra-mutated, 30.3 % as mismatch repair deficient, 11.1 % as p53 abnormal, and 48.5 % as non-specified molecular classification. Multiple classifiers were present in 3 % of cases. Integrating molecular classification into risk group calculation led to risk group migration in 11.1 % of patients: 7 moved to lower risk classes due to POLE mutations, while 4 shifted to higher risk due to p53 alterations. Applying the Betella algorithm, we can spare the POLE sequencing in 65 cases (65.7 %) and p53 immunochemistry in 17 cases (17.2 %). CONCLUSION: In conclusion, we externally validated the Betella algorithm in our population. The application of this new proposed algorithm enables assignment of the proper risk class and, consequently, the appropriate indication for adjuvant treatment, allowing for the rationalization of the resources that can be allocated otherwise, not only for the benefit of settings with low resources, but of all settings in general.

Is p53 immunohistochemistry alone useful for delineating adjuvant endometrial treatment in low-middle-income countries?

OBJECTIVE: Endometrial cancer (EC) treatment changed substantially with the introduction of molecular classification. Low-middle income (LMIC) countries will face barriers to including molecular classification to guide treatment. This study aims to analyse the value of p53 immunohistochemistry to delineate adjuvant treatment in FIGO stages I and II. METHODS: Patients with EC treated between 2010 and 2016 were retrospectively evaluated. Patients included in this analysis must have reviewed FIGO stage I/II high-grade histologies (endometrioid grade 3, serous, clear cell, carcinosarcoma, mixed and undifferentiated). Samples were subjected to p53 immunohistochemistry. Recurrence-free and overall survival were analysed using the Kaplan-Meier method and log-rank test. Cox proportional hazards regression was performed for multivariable analysis. RESULTS: From 2010 to 2016, 265 patients met the inclusion criteria. Patients with aberrant p53 (71.4 %) were associated with older age (59.7 % vs 77.8 % with more than 60 years), relapse (12.5 vs 29.6 %) and death (22.2 vs 46.7 %). The pattern of relapse was not different, with most being at extrapelvic sites (55.5 % vs 62.3 % for p53 wild type and aberrant, respectively). The median overall survival was not reached versus 92.2 months for p53 wild type and aberrant, respectively (p = 0.003). In multivariate analysis, chemotherapy decreased death (p = 0.014) in p53 aberrant tumours, a benefit not seen in the wild-type cohort (p = 0.22). CONCLUSION: This retrospective analysis corroborates the finding of worse outcomes for p53 aberrant tumours in stage I/II EC and the benefit of more aggressive adjuvant treatment (systemic therapy and radiotherapy). Although not ideal as a sole molecular marker, p53 immunohistochemistry could complement the classical anatomopathological features and be part of the decision-making process with patients in LMIC.

Melanoma in Pediatric and Young Adult Patients.

PURPOSE OF REVIEW: Melanoma in younger individuals has different clinical presentations, histologic characteristics and prognosis from older patients. This review summarizes key differences and important new insights into pediatric and young adult melanoma, as well as recent evolutions in treatment. RECENT FINDINGS: Molecular techniques have improved the classification of melanocytic neoplasms, and are especially useful in the workup of the diagnostically challenging lesions frequent in this age group. Molecular evaluation highlights differences between melanoma and atypical lesions with Spitz-like morphology, and should routinely be incorporated for diagnosing and classifying Spitzoid melanocytic to guide prognostication and treatment. Once diagnosed, the management of bona fide melanoma in children and young adults is largely similar to older patients, while the optimal management of lesions such as atypical Spitz tumors remains uncertain. Increased awareness of the presentation and diagnostic characteristics of melanoma in young individuals will allow earlier detection, and improved diagnostic techniques will allow optimum management without over- or under-treatment.

New staging of endometrial carcinoma - FIGO 2023.

AIM: To review the changes in the new version of the FIGO 2023 staging system for endometrial cancer. METHODS AND RESULTS: The new FIGO 2023 endometrial cancer staging system provides key updates for the diagnosis and treatment of endometrial cancer. An important step in diagnosis is molecular classification, which allows more accurate risk stratification for recurrence and the identification of targeted therapies. The new staging system, based on the recommendations of the international societies ESGO, ESTRO and ESP, incorporates not only the description of the pathological and anatomical extent of the disease, but also the histopathological characteristics of the tumour, including the histological type and the presence of lymphovascular space invasion. In addition, the staging system uses molecular testing to classify endometrial cancers into four prognostic groups: POLEmut, MMRd, NSMP and p53abn. Each group has its own specific characteristics and prognosis. The most significant changes have occurred in stages I and II, in which the sub-staging better reflects the biological behaviour of the tumour. This update increases the accuracy of prognosis and improves individualized treatment options for patients with endometrial cancer. CONCLUSION: The updated FIGO staging of endometrial cancer for 2023 incorporates different histologic types, tumour features, and molecular classifications to better reflect the current improved understanding of the complex nature of several endometrial cancer types and their underlying bio logic behaviour. The aim of the new endometrial cancer staging system is to better define stages with similar prognosis, allowing for more precise indication of individualised adjuvant radiation or systemic treatment, including the use of immunotherapy.

A new perspective on Endometrial Carcinoma classification and management strategies in context of molecular subtypes.

Endometrial cancer is the most common gynecological cancer and the second most prevalent female malignancy in the developed world. It is typically diagnosed in postmenopausal women, presenting with the characteristic clinical symptom of uterine abnormal bleeding. In the past, only two histological types were considered. However, it has become increasingly evident that endometrial cancer is a clinically heterogeneous disease, and this heterogeneity is closely associated with the diversity of underlying molecular alterations. The Cancer Genome Atlas classification has significantly advanced the diagnosis, risk stratification, and management of endometrial cancer by categorizing it into four molecular subgroups, each characterized by distinct mutational burdens and copy number alterations.

Gastric cancer molecular classification based on immunohistochemistry and in-situ hybridisation and mortality.

BACKGROUND AND AIMS: Gastric cancers (GC) are divided into subtypes based on molecular profile: Epstein-Barr virus (EBV)-positive, microsatellite instability (MSI), chromosomal instability (CIN) and genomically stable (GS) tumours. The prognostic impact of this classification is unclear. The aim was to evaluate whether the molecular subtypes determined using in-situ hybridisation (ISH) and immunohistochemistry (IHC) are associated with clinicopathological parameters and prognosis. METHODS AND RESULTS: The study included 503 GC patients. Based on ISH (EBV) and IHC (MSI and TP53), tumours were divided into EBV-positive, MSI, CIN (EBVneg/MSS/TP53aberrant) and GS (EBVneg/MSS/TP53wild-type) subgroups. Survival analyses with intestinal- and diffuse-type tumours were examined separately. EBV-positive tumours associated with male sex. Both EBV-positive and MSI tumours associated with intestinal type. CIN tumours associated with intestinal-type and positive lymph node status. GS tumours associated with diffuse-type and negative lymph node status. In the total cohort, no significant differences in the 5-year survival were observed. In intestinal tumours, the 5-year survival was better in EBV-positive tumours compared with GS tumours [hazard ratio (HR) = 0.57, 95% confidence interval (CI) = 0.33-0.99]. In diffuse tumours, the 5-year survival was worse in CIN tumours compared with GS tumours (HR = 1.57, 95% CI = 1.14-2.18). In radically resected diffuse tumours, the 5-year survival was worse in MSI tumours compared with GS tumours (HR = 3.26, 95% CI = 1.20-8.82). CONCLUSIONS: The molecular classification is associated with histological type but not prognosis in GC. As the prognostic effects of molecular subtypes in intestinal- and diffuse-type cancers may differ, combining histological and molecular information is recommended for future studies.

Molecular Pathology of Urothelial Carcinoma.

Urothelial carcinoma is characterized by the presence of a wide spectrum of histopathologic features and molecular alterations that contribute to its morphologic and genomic heterogeneity. It typically harbors high rates of somatic mutations with considerable genomic and transcriptional complexity and heterogeneity that is reflective of its varied histomorphologic and clinical features. This review provides an update on the recent advances in the molecular characterization and novel molecular taxonomy of urothelial carcinoma and variant histologies.

Understanding the intrinsic biology of diffuse large B-cell lymphoma: recent advances and future prospects.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoid tumor, and accounts for approximately 30-40% of non-Hodgkin lymphomas. Although the prognosis has significantly improved with the advent of rituximab combination chemotherapy in the early 2000s, recurrence still occurs in about 40% of cases. Even though chemotherapy with increased dose-intensity is used in recurrent cases, the prognosis of such patients remains poor. Thus, the development of personalized medicine, including molecular-targeted drugs, is required to improve the prognosis of DLBCL patients, and further understanding of the molecular pathogenesis of DLBCL is essential for this purpose. With recent advances in genetic analysis technology, unknown genetic abnormalities and gene expression patterns have been discovered, and based on these discoveries, progress is being made in elucidating and subdividing molecular pathologies. This article summarizes recent findings regarding molecular pathogenesis in DLBCL using transcriptome and genomics technologies, and outlines the path to personalized medicine.

Molecular profile in endometrial carcinoma: can we predict the lymph node status? A systematic review and meta-analysis.

PURPOSE: Molecular classification of endometrial cancer (EC) has become a promising information to tailor preoperatively the surgical treatment. We aimed to evaluate the rate of lymph node metastases (LNM) in patients with EC according to molecular profile. METHODS: A systematic review and meta-analysis were performed according to PRISMA guidelines by searching in two major electronic databases (PubMed and Scopus), including original articles reporting lymph node metastases according to the molecular classification of EC as categorized in the ESGO-ESMO-ESP guidelines. RESULTS: Fifteen studies enrolling 3056 patients were included. Pooled prevalence LNM when considering only patients undergoing lymph node assessment was 4% for POLE-mutated (95%CI: 0-12%), 22% for no specific molecular profile (95% CI: 9-39%), 23% for Mismatch repair-deficiency (95%CI: 10-40%) and 31% for p53-abnormal (95%CI: 24-39%). CONCLUSIONS: The presence of LNM seems to be influenced by molecular classification. P53-abnormal group presents the highest rate of nodal involvement, and POLE-mutated the lowest.

Molecular classification and intratumoral heterogeneity of gastric adenocarcinoma.

Gastric cancers frequently harbor striking histological complexity and diversity between lesions as well as within single lesions, known as inter- and intratumoral heterogeneity, respectively. The latest World Health Organization Classification of Tumors designated more than 30 histological subtypes for gastric epithelial tumors, assigning 12 subtypes for gastric adenocarcinoma (GAD). Meanwhile, recent advances in genome-wide analyses have provided molecular aspects to the histological classification of GAD, and consequently revealed different molecular traits underlying these histological subtypes. Moreover, accumulating knowledge of comprehensive molecular profiles has led to establishing molecular classifications of GAD, which are often associated with clinical biomarkers for therapeutics and prognosis. However, most of our knowledge of GAD molecular profiles is based on inter-tumoral heterogeneity, and the molecular profiles underlying intratumoral heterogeneity are yet to be determined. In this review, recently established molecular classifications of GAD are introduced in the aspect of pathological diagnosis and are discussed in the context of intratumoral heterogeneity.

Abnormal p53 expression is associated with poor outcomes in grade I or II, stage I, endometrioid carcinoma: a retrospective single-institute study.

OBJECTIVE: The Cancer Genome Atlas study revealed an association between copy-number high (p53 abnormal) genetic mutation and poor prognosis in endometrial cancer in 2013. This retrospective study investigated outcomes in patients with abnormal p53 expression and stage I, low-grade endometrial endometrioid carcinoma (EEC). METHODS: We enrolled women with stage I, grade 1 or 2 EEC who received comprehensive staging and adjuvant therapy between January 2019 and December 2022 at MacKay Memorial Hospital, Taipei, Taiwan. Pathologists interpreted immunohistochemistry stains of cancerous tissues to detect p53 mutation. We compared recurrence, survival, progression-free survival, and overall survival between p53 abnormal and p53 normal groups. RESULTS: Of the 115 patients included, 26 had pathologically confirmed abnormal p53 expression. Of these 26 patients, five (19.2%) experienced recurrence, and two died due to disease progression. By contrast, no patients in the normal p53 group experienced disease recurrence or died due to disease progression. Significant intergroup differences were discovered in recurrent disease status (19.4% vs. 0%, p<0.001), mortality (7.7% vs. 0%, p<0.001), and progression-free survival (p<0.001). The overall survival (p=0.055) also showed powerful worse trend. CONCLUSION: For patients with stage I, low-grade EEC, abnormal p53 expression may be used as an indicator of poor prognosis. Therefore, we suggest considering aggressive adjuvant therapies for these patients.