Predicting poor prognosis recurrence in women with endometrial cancer: a nomogram developed by the FRANCOGYN study group

New online dynamic nomograms to predict recurrence-free and overall survival after resection of endometrial cancer: a single-institution retrospective cohort study

PURPOSE

The significant global burden of endometrial cancer (EC) and the challenges associated with predicting EC recurrence indicate the need for a dynamic prediction model. This study aimed to propose nomograms based on clinicopathological variables to predict recurrence-free survival (RFS) and overall survival (OS) after surgical resection for EC.

METHODS

This single-institution retrospective cohort study included patients who underwent surgical resection for EC. Web-based nomograms were developed to predict RFS and OS following resection for EC, and their discriminative and calibration abilities were assessed.

RESULTS

This study included 289 patients (median age, 56 years). At a median follow-up of 51.1 (range, 4.1-128.3) months, 13.5% (39/289) of patients showed relapse or died, and 10.7% (31/289) had non-endometrioid tumors (median size: 2.8 cm). Positive peritoneal cytology result (hazard ratio [HR], 35.06; 95% confidence interval [CI], 1.12-1095.64; P = 0.0428), age-adjusted Charlson comorbidity index (AACCI) (HR, 52.08; 95% CI, 12.35-219.61; P < 0.001), and FIGO (Federation of Gynecology and Obstetrics) stage IV (HR, 138.33; 95% CI, 17.38-1101.05; P < 0.001) were predictors of RFS. Similarly, depth of myometrial invasion ≥ 1/2 (HR, 1; 95% CI, 0.46-2.19; P = 0.995), AACCI (HR, 93.63; 95% CI, 14.87-589.44; P < 0.001), and FIGO stage IV (HR, 608.26; 95% CI, 73.41-5039.66; P < 0.001) were predictors of OS. The nomograms showed good predictive capability, positive discriminative ability, and calibration (RFS: 0.895 and OS: 0.891).

CONCLUSION

The nomograms performed well in internal validation when patients were stratified into prognostic groups, offering a personalized approach for risk stratification and treatment decision-making.

Development and validation of a prognostic nomogram model incorporating routine laboratory biomarkers for preoperative patients with endometrial cancer

BACKGROUND

Some biomarkers collected from routine laboratory tests have shown important value in cancer prognosis. The study aimed to evaluate the prognostic significance of routine laboratory biomarkers in patients with endometrial cancer (EC) and to develop credible prognostic nomogram models for clinical application.

METHODS

A total of 727 patients were randomly divided into a training set and a validation set. Cox proportional hazards models were used to evaluate each biomarker's prognostic value, and independent prognostic factors were used to generate overall survival (OS) and progression-free survival (PFS) nomgrams. The efficacy of the nomograms were evaluated by Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration curves, X-tile analysis and Kaplan‒Meier curves.

RESULTS

Ten significant biomarkers in multivariate Cox analysis were integrated to develop OS and PFS nomograms. The C-indices of the OS- nomogram in the training and validation sets were 0.885 (95% confidence interval (CI), 0.810-0.960) and 0.850 (95% CI, 0.761-0.939), respectively; those of the PFS- nomogram in the training and validation sets were 0.903 (95% CI, 0.866-0.940) and 0.825 (95% CI, 0.711-0.939), respectively. ROC, DCA and calibration curves showed better clinical application value for the nomograms incorporating routine laboratory biomarkers. X-tile analysis and Kaplan‒Meier curves showed that the nomograms were stable and credible in evaluating patients at different risks.

CONCLUSIONS

Nomogram models incorporating routine laboratory biomarkers, including NLR, MLR, fibrinogen, albumin and AB blood type, were demonstrated to be simple, reliable and favourable in predicting the outcomes of patients with EC.

Development and validation of a nomogram for predicting recurrence-free survival in endometrial cancer: a multicenter study

Recurrence is the main cause of death in patients with endometrial cancer (EC). This study aimed to construct and validate a nomogram to predict the recurrence-free survival of patients with EC. This was a multicenter retrospective study. A total of 812 patients from Wuhan Tongji Hospital were divided into training and validation cohorts, and 347 and 580 patients from People's Hospital of Peking University and Qilu Hospital of Shandong, respectively, were used for validation. Univariate and multivariate Cox regression analyses were used to construct a nomogram for predicting recurrence-free survival of EC. Calibration curves, receiver operating characteristic (ROC) curves, and consistency indexes (C-indexes) were used to estimate the performance of the model. Decision curve analysis (DCA) curves were used to assess the clinical utility of the model. Age (P = 0.013), cancer antigen 125 level (P = 0.014), lymphovascular space invasion (P = 0.004), International Federation of Gynecology and Obstetrics stage (P = 0.034), and P53 (P < 0.001) were independently associated with recurrence, and we constructed a nomogram based on these variables. The C-indexes of the validation cohorts were 0.880, 0.835, and 0.875, respectively. The calibration, ROC, and DCA curves revealed that this model had excellent performance and clinical utility. Combining clinical data, clinicopathological factors, serological indicators, and immunohistochemical marks, a multicenter externally verified nomogram with robust performance was constructed to predict the recurrence of patients with EC.

A nomogram for predicting recurrence in endometrial cancer patients: a population-based analysis

Objective

Endometrial cancer recurrence is one of the main factors leading to increased mortality, and there is a lack of predictive models. Our study aimed to establish a nomogram predictive model to predict recurrence in endometrial cancer patients.

Method

Screen 517 endometrial cancer patients who came to Nanjing Drum Tower Hospital from 2008 to 2018. All these data are listed as the training group, and then 70% and 60% are randomly divided into verification groups 1 and 2. Univariate, Multivariate logistic regression, stepwise regression were used to select variables for nomogram. Nomogram identification and calibration were evaluated by concordance index (c-index), area under receiver operating characteristic curve (AUC) over time and calibration plot Function. By decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination improvement (IDI), we compared and quantified the net benefit of nomogram and ESMO-ESGO-ESTRO model-based prediction of tumor recurrence.

Results

A nomogram predictive model of endometrial cancer recurrence was established with the eight variables screened. The c-index (for the training cohort and for the validation cohort) and the time-dependent AUC showed good discriminative power of the nomogram. Calibration plots showed good agreement between nomogram predictions and actual observations in both the training and validation sets.

Conclusions

We developed and validated a predictive model of endometrial cancer recurrence to assist clinicians in assessing recurrence in endometrial cancer patients.

Evolving treatment paradigms in metastatic or recurrent low-grade endometrial cancer: When is hormonal-based therapy the preferred option?

Endometrial cancer is the most common gynecologic malignancy in developed countries, with increasing incidence and mortality rates worldwide. While most cases are successfully treated with surgery, first-line treatment options for metastatic or recurrent endometrial cancer involve significant toxicities. Imprecise classification of heterogeneous subgroups further complicates treatment decisions and interpretation of clinical trial results. Recent advances in molecular classification are guiding treatment decisions for metastatic or recurrent endometrial cancers. Integrating molecular characteristics with traditional clinicopathology can both reduce overtreatment or undertreatment and help guide the appropriate choice of therapies and effective design of future studies. Here we discuss the treatment of metastatic or recurrent low-grade endometrioid adenocarcinoma of the uterine corpus, which is distinct from high-grade tumors histologically, molecularly, and in treatment response.

The relationship between serum CA-125 level and recurrence in surgical stage I endometrial cancer patients

BACKGROUND

The majority of patients diagnosed with early stage endometrial cancer have a favorable prognosis; however, approximately 10% to 15% experience a recurrence. Therefore, the aim of the present study was to evaluate whether postoperative carbohydrate antigen 125 (CA-125) levels could be used to predict recurrence and recurrence-free survival (RFS) in patients with surgical stage I endometrial cancer.

METHODS

We enrolled a total of 518 patients with stage I endometrial cancer who underwent surgical treatment between January 2010 and March 2019. Serum CA-125 levels were measured prior to surgery, as well as 6 to 12 months after surgery. Subsequently, the correlations between the CA-125 levels, cancer recurrence, and RFS were analyzed.

RESULTS

Although the preoperative CA-125 level was not associated with the risk of cancer recurrence, the postoperative CA-125 level was found to be the only independent predictor of recurrence in both univariate and multivariate analyses. Additionally, we found that a postoperative CA-125 cutoff value of 13.75 U/mL yielded the best sensitivity and specificity for predicting cancer recurrence. Patients with a postoperative CA-125 level ≥13.75 U/mL, and those with a level <13.75 U/mL, had a median time to recurrence and a 5-year RFS rate of 35.5 vs 50.5 months and 84.7 vs 94.4%, respectively. Additionally, postoperative CA-125 levels were not found to be correlated with preoperative levels.

CONCLUSION

In patients with stage I endometrial cancer, a postoperative CA-125 level ≥13.75 U/mL was found to be significantly correlated with a higher recurrence rate, as well as a shorter RFS. Therefore, obtaining a follow-up CA-125 level within 6 to 12 months after staging surgery may be a promising noninvasive biomarker for predicting recurrence.

YAP1 affects the prognosis through the regulation of stemness in endometrial cancer

Background

Endometrial cancer stem-like cells (ECSCs) have been proven to be responsible for recurrence, metastasis, and drug-resistance in patients with endometrial cancer. The HIPPO pathway has been shown to play an important role in the development and maintenance of stemness in a variety of tumors. While there was less research about its function in ECSCs. The aim of this study was to explore the role of YAP1, a core molecular of HIPPO pathway, in the stemness of endometrial cancer and to reveal its influence on prognosis.

Methods

We collected specimens and clinical data from 774 patients with endometrial cancer to analyze the correlation between YAP1 expression and prognosis. We then examined the expression of YAP1 in ECSCs and EC cell lines (Ishikawa; HEC1-A) in vitro experiments. Changes in the stemness of cell lines were detected after YAP1 silencing by siRNA. Finally, high-throughput sequencing was used to predict the potential molecular interactions and mechanisms of YAP1's effect on stemness.

Result

Down-regulation of YAP1 significantly suppresses the stemness of EC cell lines. High expression of YAP1 leads to poor prognosis in EC by regulation of stemness.

Conclusion

YAP1 plays an important role in the prognosis of patients with EC by regulation of stemness.

Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study

OBJECTIVES

To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC).

METHODS

A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence.

RESULTS

The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence.

CONCLUSIONS

The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients.

CLINICAL RELEVANCE STATEMENT

An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients.

KEY POINTS

• The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.

Menopausal Status Contributes to Overall Survival in Endometrial Cancer Patients

Endometrial cancer is the most common female genital tract malignancy in developed countries that occurs predominantly in postmenopausal women. The primary objective of our research was to investigate whether menopause status together with selected conventional prognostic indicators may contribute to overall (all-cause) survival in endometrial cancer patients. For this purpose, we applied the Cox proportional hazards regression model. Patients in advanced FIGO stage showed a relatively poor survival rate. The time since last menstruation and postoperative FSH concentration were identified as unfavorable prognostic factors in our model. Additionally, age at diagnosis, BMI value, adjuvant treatment (brachytherapy), and parity showed no impact on survival. To our knowledge, this is the first study to report a prognostic model for endometrial cancer including exact time from last menstruation as one of the prognostic variables. Due to the fact that there are no stratifying systems to reliably predict survival in patients with endometrial cancer, there is a strong need to revise and update existing models using complementary prognostic indicators. Collection of precise data on various risk factors may contribute to increased accuracy of artificial intelligence algorithms in order to personalize cancer care in the near future.

Development and validation of a nomogram involving immunohistochemical markers for prediction of recurrence in early low-risk endometrial cancer

Background

The purpose of this study was to construct a nomogram based on classical parameters and immunohistochemical markers to predict the recurrence of early low-risk endometrial cancer patients.

Methods

A total of 998 patients with early low-risk endometrial cancer who underwent primary surgical treatment were enrolled (668 in the training cohort, 330 in the validation cohort). Prognostic factors identified by univariate and multivariate analysis in the training cohort were used to construct the nomogram. Prediction performance of the nomogram was evaluated using the calibration curve, concordance index (C-index), and the time-dependent receiver operating characteristic curve. The cumulative incidence curve was used to describe the prognosis of patients in high-risk and low-risk groups divided by the optimal risk threshold of the model.

Results

In the training cohort, grade ( P = 0.040), estrogen receptor ( P < 0.001), progesterone receptor ( P = 0.001), P53 ( P = 0.004), and Ki67 ( P = 0.002) were identified as independent risk factors of recurrence of early low-risk endometrial cancer, and were used to establish the nomogram. The calibration curve showed that the fitting degree of the model was good. The C-indexes of training and validation cohorts were 0.862 and 0. 827, respectively. Based on the optimal risk threshold of the nomogram, patients were split into a high-risk group and a low-risk group. The cumulative incidence curves showed that the prognosis of the high-risk group was far worse than that of the low-risk group ( P < 0.001).

Conclusion

This nomogram, with a combination of classical parameters and immunohistochemical markers, can effectively predict recurrence in early low-risk endometrial cancer patients.

Predicting recurrence and recurrence-free survival in high-grade endometrial cancer using machine learning

OBJECTIVE

To develop machine-learning models to predict recurrence and time-to-recurrence in high-grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.

METHODS

Data were retrospectively collected across eight Canadian centers including 1237 patients. Four models were trained to predict recurrence: random forests, boosted trees, and two neural networks. Receiver operating characteristic curves were used to select the best model based on the highest area under the curve (AUC). For time to recurrence, we compared random forests and Least Absolute Shrinkage and Selection Operator (LASSO) model to Cox proportional hazards.

RESULTS

The random forest was the best model to predict recurrence in HGEC; the AUCs were 85.2%, 74.1%, and 71.8% in the training, validation, and test sets, respectively. The top five predictors were: stage, uterus height, specimen weight, adjuvant chemotherapy, and preoperative histology. Performance increased to 77% and 80% when stratified by Stage III and IV, respectively. For time to recurrence, there was no difference between the LASSO and Cox proportional hazards models (c-index 71%). The random forest had a c-index of 60.5%.

CONCLUSIONS

A bootstrap random forest model may be a more accurate technique to predict recurrence in HGEC using multiple clinicopathologic factors. For time to recurrence, machine-learning methods performed similarly to the Cox proportional hazards model.

Estrogen Receptor- And Progesterone Receptor-Positive Thresholds in Predicting the Recurrence of Early Low-Risk Endometrial Cancer

Background

Estrogen receptors (ER) and progesterone receptors (PR) have important prognostic value in endometrial cancer, but there is no recognized positive immunohistochemical threshold for predicting the recurrence of early low-risk endometrial cancer. The purpose of this study was to clarify the optimal positive thresholds of the immunohistochemical parameters the ER and PR in early low-risk endometrial cancer.

Methods

A total of 332 patients with stage IA endometrial cancer were enrolled from the First Affiliated Hospital of Chongqing Medical University and People's Hospital of Sha ping ba District in Chongqing between January 2013 and December 2018. First, univariate and multivariate Cox regression analyses were used to analyze the correlation between various clinical factors and the prognosis of early low-risk endometrial cancer. Then, the receiver operating characteristic curve (ROC curve) and Youden index were used to determine the positive thresholds of ER and PR.

Results

The positive thresholds of ER and PR for predicting the recurrence of early low-risk endometrial cancer were 12% and 8%, respectively. Multivariate analysis showed that ER (P = 0.004), PR (P = 0.026), and p53 (P = 0.021) were risk factors for the prognosis of patients with early low-risk endometrial cancer. The recurrence-free survival and the overall survival in the low ER group and PR group were much lower than those in the high ER group and PR group (P < 0.001 of all).

Conclusions

ER and PR positive thresholds of 12% and 8%, respectively, are the most suitable for predicting the recurrence of early low-risk endometrial cancer.

Machine learning methods to predict presence of residual cancer following hysterectomy

Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despite maximal surgical and medical care, gynecologic malignancies have a high rate of recurrence following surgery. Current machine learning models use advanced pathology data that is often inaccessible within low-resource settings and are specific to singular cancer types. There is currently a need for machine learning models to predict non-clinically evident residual disease using only clinically available health data. Here we developed and tested multiple machine learning models to assess the risk of residual disease post-hysterectomy based on clinical and operative parameters. Data from 3656 hysterectomy patients from the NSQIP dataset over 14 years were used to develop models with a training set of 2925 patients and a validation set of 731 patients. Our models revealed the top postoperative predictors of residual disease were the initial presence of gross abdominal disease on the diaphragm, disease located on the bowel mesentery, located on the bowel serosa, and disease located within the adjacent pelvis prior to resection. There were no statistically significant differences in performances of the top three models. Extreme gradient Boosting, Random Forest, and Logistic Regression models had comparable AUC ROC (0.90) and accuracy metrics (87–88%). Using these models, physicians can identify gynecologic cancer patients post-hysterectomy that may benefit from additional treatment. For patients at high risk for disease recurrence despite adequate surgical intervention, machine learning models may lay the basis for potential prospective trials with prophylactic/adjuvant therapy for non-clinically evident residual disease, particularly in under-resourced settings.

Conditional relative survival of patients with endometrial cancer: a Korean National Cancer Registry study

Objective

The purpose of this study was to estimate 5-year conditional relative survival (5Y CRS) rates of endometrial cancer (EC) in Korea accounting for time already survived. Subgroup-specific estimates stratified by various patient characteristics were also presented.

Methods

Using the data from the Korean Central Cancer Registry, 5Y CRS rates were calculated in patients who were diagnosed with EC between 1998 and 2017. The CRS rates were presented by year of diagnosis, age at diagnosis, histology, cancer stage, and treatment received.

Results

The 5-year relative survival rate at the time of diagnosis was 89.0% for all cases. The probability of surviving an additional 5 years (i.e., 5Y CRS), if the patient survived 1, 2, 3, 4, and 5 years after diagnosis was 91.8%, 94.1%, 95.6%, 96.5%, and 97.3%, respectively. Patients with poor initial prognoses, i.e., those who were older, had non-endometrioid histology, and high stage, showed the largest improvements in 5Y CRS, reaching >90% for most subgroups, except those with serous histology (88.4%) and distant stage (77.7%). Patients aged ≥70 years had the highest probability of death in the 1st and 2nd years after diagnosis (13.8 and 11.0%), but the conditional probability of death in the 3rd, 4th, and 5th years declined rapidly to 7.3%, 4.5%, and 3.7%, respectively.

Conclusion

The CRS rates for patients with EC improved with increased time elapsed from diagnosis. The greatest improvements in 5Y CRS were observed among patients who were older, those with non-endometrioid histology, and those with more advanced disease.

There is paucity of research which estimated the conditional relative survival (CRS) rates for patients with endometrial cancer, and the Asian population is underrepresented in these studies, despite that Asian patients have different age and histologic distribution. CRS rates were presented by year of diagnosis, age at diagnosis, histology, cancer stage, and treatment received. Such estimates based on patient's evolving risk profile will provide updated prognostic information useful for both patients and healthcare providers.

A prediction model based on clinical and histological features for predicting recurrence in patients with stage I-II endometrial cancer after surgical treatment

OBJECTIVE

The study aimed to develop a prediction model combining clinical and histological features to predict recurrence in patients with stage I-II endometrial cancer (EC) after surgical treatment.

METHODS

A total of 746 stage I-II EC patients who had received primary surgical treatment at Taizhou People's Hospital between 2014 and 2018 were included and randomly divided as a Training cohort (n = 520) and a Validation cohort (n = 226) at a 7:3 ratio. Clinical features including age, body mass index, comorbidities, lymphadenectomy, and adjuvant treatment, and histological features including histologic type, myometrial invasion, cervical stromal invasion, and expression levels of Ki67, estrogen receptor (ER), progesterone receptor (PR), whey acidic protein 4-disulphide core domain 2 (WFDC2), and p53 were used to develop a prediction model for EC recurrence in the Training cohort using a multivariable Cox regression model. Model discrimination and calibration were further evaluated in the Validation cohort.

RESULTS

EC recurrence was observed in 60 (11.54%) patients in the Training cohort with a median length of follow-up of 39 months. Age, adjuvant treatment, histologic type, cervical stromal invasion, and expression levels of Ki67, ER, PR, and WFDC2 were factors significantly associated with EC recurrence based on univariable Cox regression analysis. After a model selection by AIC in a stepwise algorithm, the final model incorporated the above predictors showed a C-index of 0.85 and fair calibration in the Training cohort. In the Validation cohort, the model still showed good discrimination power (C-index 0.80) but moderate calibration.

CONCLUSIONS

The developed prediction model combining clinical and histological features can help to predict the EC recurrence in patients with stage I-II EC after surgical treatment.

Combining Clinicopathological Parameters and Molecular Indicators to Predict Lymph Node Metastasis in Endometrioid Type Endometrial Adenocarcinoma

Background

Lymph node metastasis (LNM) is a critical unfavorable prognostic factor in endometrial cancer (EC). At present, models involving molecular indicators that accurately predict LNM are still uncommon. We addressed this gap by developing nomograms to individualize the risk of LNM in EC and to identify a low-risk group for LNM.

Methods

In all, 776 patients who underwent comprehensive surgical staging with pelvic lymphadenectomy at the First Affiliated Hospital of Chongqing Medical University were divided into a training cohort (used for building the model) and a validation cohort (used for validating the model) according to a predefined ratio of 7:3. Logistics regression analysis was used in the training cohort to screen out predictors related to LNM, after which a nomogram was developed to predict LNM in patients with EC. A calibration curve and consistency index (C-index) were used to estimate the performance of the model. A receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of the risk probability of LNM predicted by the model proposed in this study. Then, the prediction performance of different models and their discrimination abilities for identifying low-risk patients were compared.

Result

LNM occurred in 87 and 42 patients in the training and validation cohorts, respectively. Multivariate logistic regression analysis showed that histological grade (P=0.022), myometrial invasion (P=0.002), lymphovascular space invasion (LVSI) (P=0.001), serum CA125 (P=0.008), Ki67 (P=0.012), estrogen receptor (ER) (0.009), and P53 (P=0.003) were associated with LNM; a nomogram was then successfully established on this basis. The internal and external calibration curves showed that the model fits well, and the C-index showed that the prediction accuracy of the model proposed in this study was better than that of the other models (the C-index of the training and validation cohorts was 0.90 and 0.91, respectively). The optimal threshold of the risk probability of LNM predicted by the model was 0.18. Based on this threshold, the model showed good discrimination for identifying low-risk patients.

Conclusion

Combining molecular indicators based on classical clinical parameters can predict LNM of patients with EC more accurately. The nomogram proposed in this study showed good discrimination for identifying low-risk patients with LNM.

Inhibiting Wnt/beta-catenin in CTNNB1-mutated endometrial cancer

The role of β-catenin/TCF transcriptional activity in endometrial cancer (EC) recurrence is not well understood. We assessed the impact of Wnt/β-catenin inhibition in EC models. In an analysis of the Cancer Genome Atlas, we confirmed that CTNNB1 mutations are enriched in recurrent low-risk EC and showed that aberrant Wnt/β-catenin pathway activation is associated with recurrence. We studied CTNNB1-wildtype (HEC1B, Ishikawa) and CTNNB1-mutant (HEC108, HEC265, HEC1B-S33Y, Ishikawa-S33Y) EC cell lines. Dose response curves were determined for 5 Wnt/β-catenin pathway inhibitors (Wnt-C59, XAV-939, PyrPam, PRI-724, SM04690). XAV939, Wnt-C59 and PyrPam inhibited function upstream of β-catenin transcriptional activity and were ineffective at inhibiting cell viability. In contrast, PRI724 and SM04690 indirectly inhibited β-catenin transcriptional activity and significantly reduced cell viability in CTNNB1-mutant cell lines. Treatment with SM04690 reduced cell viability (Licor Cell stain) in all EC cell lines, but viability was significantly lower in CTNNB1-mutant cell lines (p < 0.01). Mechanistically, SM04690 significantly inhibited proliferation measured via 5'-bromo-2'-deoxyuridine incorporation and reduced T cell factor (TCF) transcriptional activity. HEC1B, HEC1B-S33Y and HEC265 tumor-bearing mice were treated with vehicle or SM04690. Tumors treated with SM04690 had smaller mean volumes than those treated with vehicle (p < 0.001, p = 0.014, p = 0.06). In HEC1B-S33Y and HEC265 tumors, SM04690 treatment significantly reduced Ki67 H-scores compared to vehicle (p = 0.035, p = 0.024). Targeting the Wnt/β-catenin pathway in CTNNB1-mutant EC effectively inhibited proliferation and β-catenin/TCF transcriptional activity and blunted tumor progression in in vivo models. These studies suggest β-catenin transcriptional inhibitors are effective in EC and particularly in CTNNB1-mutant EC, highlighting a potential therapeutic vulnerability for treatment of CTNNB1-mutant EC.

[Using Immunohistochemical Markers and Clinicopathological Factors to Predict the Prognostic Survival of Different Types of Endometrial Cancer Recurrence]

OBJECTIVE

To probe for factors that can be used effectively to predict the prognostic survival of patients with endometrial cancer recurrence.

METHODS

The clinicopathological data of 473 patients with stage Ⅰ to Ⅲ endometrial cancer who underwent standard surgical treatment from October 2013 to May 2019 were retrospectively collected, and post-operative recurrence of the patients were followed up. Overall recurrence includes local recurrence and poor prognosis recurrence. The endpoint indicators of this study are the recurrence-free survival (RFS) and overall survival (OS) of patients with overall recurrence, local recurrence, and poor prognosis recurrence (PPR). The Kaplan-Meier survival curve was used to evaluate the OS and RFS of patients. Cox proportional-hazards model was used to identify factors affecting the prognostic survival of patients with endometrial cancer recurrence.

RESULTS

Among the 473 patients, 406 did not experience recurrence. A total of 67 patients, accounting for 14.2%, had recurrence. Among them, 27 had local recurrence, accounting for 5.7%, while 40 had poor prognosis recurrence, accounting for 8.5%. The median follow-up time of patients with recurrence was 38 months. The survival curve showed that the RFS and OS of the patients in the recurrence-free group remained unchanged, while the patients in the recurrence group, regardless of whether they had overall recurrence, local recurrence or PPR, experienced a decrease in RFS and OS( P<0.001). The overall 3-year OS rate of patients with recurrence was 44.8%, the median survival time was 29 months, and the median recurrence time was 17 months. The 3-year OS rate of patients in the recurrence-free group was 98.8%, and the median survival time was 40 months; the 3-year OS rate of patients with local recurrence was 59.3%, the median survival time was 27 months, and the median recurrence time was 15 months. The 3-year OS rate of patients with PPR was only 35.0%, the median survival time was 22 months, and the median recurrence time was 10 months. The results of multivariate Cox regression analysis showed that, for overall recurrence patients, FIGO stage Ⅲ (hazard ratio ( HR)=3.432, P=0.005), increased expression of K-i67 ( HR=1.015, P=0.025), and decreased expression of estrogen receptor (ER) ( HR=0.985, P=0.005) are independent factors for the decline in RFS, FIGO stage Ⅲ ( HR=4.918, P=0.005) and the decreased expression of progesterone receptor (PR) ( HR=0.977, P=0.003) are independent factors for the decrease in OS. For patients with local recurrence, special pathological types ( HR=2.545, P=0.049) and increased expression of Ki-67 ( HR=1.024, P=0.033) are independent factors influencing the decrease in RFS, while decreased expression of PR ( HR=0.973, P=0.009) is an independent risk factor for decreased OS. For patients with PPR, FIGO stage Ⅲ ( HR=5.977, P=0.002) and decreased ER expression ( HR=0.984, P=0.023) are independent risk factors for the decline in RFS, while FIGO stage Ⅲ ( HR=10.098, P=0.001) is an independent factor influencing the decline of OS.

CONCLUSION

FIGO stage Ⅲ, increased Ki-67 expression, and decreased ER expression can increase patients' risk of postoperative recurrence, and FIGO stage Ⅲ and decreased expression of PR can increase the risk of death in patients with recurrence.

Competing risk nomogram predicting cancer-specific mortality for endometrial cancer patients treated with hysterectomy

Background

The incidence of endometrial cancer has tended to increase in recent years. However, competing risk nomogram combining comprehensive factors for endometrial cancer patients treated with hysterectomy is still scarce. Therefore, we aimed to build a competing risk nomogram predicting cancer‐specific mortality for endometrial cancer patients treated with hysterectomy.

Methods

Patients diagnosed with endometrial cancer between 2010 and 2012 were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database. Competing risk model was performed to select prognostic variables to build the competing risk nomogram to predict the cumulative 3‐ and 5‐year incidences of endometrial cancer‐specific mortality. Harrell's C‐index, receiver operating characteristic (ROC) curve, and calibration plot were used in the internal validation. And decision curve analysis was applied to evaluate clinical utility.

Results

A total of 10,447 patients were selected for analysis. The competing risk nomogram identified eight prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, and number of regional nodes positive. The C‐index of the competing risk nomogram was 0.857 (95% confidence interval [CI]: 0.854–0.859), and the calibration plots were adequately fitted. When the threshold probabilities were between 1% and 57% for 3‐year prediction and between 2% and 67% for 5‐year prediction, the competing risk nomogram was of good clinical utility.

Conclusions

A competing risk nomogram for endometrial cancer patients treated with hysterectomy was successfully built and internally validated. It was an accurately predicted and clinical useful tool, which could play an important role in consulting and health care management of endometrial cancer patients.

Recurrence risk factors in stage IA grade 1 endometrial cancer

OBJECTIVES

Patients with early-stage endometrial cancers (EC) with disease recurrences have worse survival outcomes. The purpose of this study was to identify clinical and pathologic factors that predict for all recurrences in stage IA grade 1 (IAG1) EC.

METHODS

Records from patients diagnosed with EC were retrospectively reviewed. Baseline characteristics of 222 patients with IAG1 EC who underwent surgical resection were analyzed. Cox proportional hazard analysis was used to identify univariate and multivariate risk factors that predict for recurrence.

RESULTS

Seventeen (7.65%) patients had recurrences. The 3-year cumulative incidence of recurrence were significantly higher for patients with time from biopsy to surgery ≥6 months (54% vs. 8%, p=0.003), simple hysterectomy with ovarian preservation vs. total hysterectomy and bilateral salpingo-oophorectomy (31% vs. 9%, p=0.032), any myometrial invasion vs. no invasion (18% vs. 2%, p=0.004), and tumor size ≥2 cm (15% vs. 2%, p=0.021). On, multivariate analysis, any myometrial invasion, increasing time from biopsy to surgery, and larger tumor size were independent predictors of any recurrence. Patients with recurrences had worse outcomes than those without (5-year overall survival [OS]=60%; 95% confidence interval [CI]=16%-86% vs. 5-year OS=95%; 95% CI=87%-99%, respectively, p=0.003).

CONCLUSION

Time from biopsy to surgery, larger tumors, and myometrial invasion are the most important predictors of recurrence. Though the recurrence rates are generally low in IAG1 EC, the survival rate for the patients with recurrences was worse than those without. Identification of additional recurrence risk factors can help select patients who may benefit from adjuvant treatment.

A Nomogram Model Involving Immunohistochemical Markers for Predicting the Recurrence of Stage I-II Endometrial Cancer

Background

The purpose of this study was to establish a nomogram combining classical parameters and immunohistochemical markers to predict the recurrence of patients with stage I-II endometrial cancer (EC).

Methods

419 patients with stage I-II endometrial cancer who received primary surgical treatment at the First Affiliated Hospital of Chongqing Medical University were involved in this study as a training cohort. Univariate and multivariate Cox regression analysis of screening prognostic factors were performed in the training cohort to develop a nomogram model, which was further validated in 248 patients (validation cohort) from the Second Affiliated Hospital of Chongqing Medical University. The calibration curve was used for internal and external verification of the model, and the C-index was used for comparison among different models.

Results

There were 51 recurrent cases in the training cohort while 31 cases in the validation cohort. Univariate analysis showed that age, histological type, histological grade, myometrial invasion, cervical stromal invasion, postoperative adjuvant treatment, and four immunohistochemical makers (Ki67, estrogen receptor, progesterone receptor, P53) were the related factors for recurrence of EC. Multivariate analysis demonstrated that histological type (P = 0.029), myometrial invasion (P = 0.003), cervical stromal invasion (P = 0.001), Ki67 (P < 0.001), ER (P = 0.009) and P53 expression (P = 0.041) were statistically correlated with recurrence of EC. Recurrence-free survival was better predicted by the proposed nomogram with a C-index of 0.832 (95% CI, 0.752–0.912) in the training cohort, and the validation set confirmed the finding with a C-index of 0.861 (95% CI, 0.755–0.967).

Conclusion

The nomogram model combining classical parameters and immunohistochemical markers can better predict the recurrence in patients with FIGO stage I-II EC.

Development and Validation of Nomograms Predicting the Overall and the Cancer-Specific Survival in Endometrial Cancer Patients

Background: The present study was aimed at developing nomograms estimating the overall survival (OS) and cancer-specific survival (CSS) of endometrial cancer (EC)-affected patients.

Patients and Methods: We retrospectively collected 145,445 EC patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified via univariate and multivariate Cox analyses. These risk factors were used to establish nomograms to predict 3- and 5-year OS and CSS rates. Internal and external data were used for validation. The predictive accuracy and discriminative ability were measured by using concordance index (C-index) and risk group stratification.

Results: A total of 63,510 patients were collected and randomly assigned into the training cohort (n = 42,340) and the validation cohort (n = 21,170). Age at diagnosis, marital status, tumor size, histologic type, lymph node metastasis, tumor grade, and clinical stage were identified as independent prognostic factors for OS and CSS (p < 0.05 according to multivariate Cox analysis) and were further used to construct the nomograms. The area under the receiver operating characteristics (ROC) curve was greater than that of International Federation of Gynecology and Obstetrics (FIGO) staging system for predicting OS (0.83 vs. 0.73, p < 0.01) and CSS (0.87 vs. 0.79, p < 0.01) in the training cohort. The stratification into different risk groups ensured a significant distinction between survival curves within different FIGO staging categories.

Conclusion: We constructed and validated nomograms that accurately predicting OS and CSS in EC patients. The nomograms can be used for estimating OS and CSS of individual patients and establishing their risk stratification.

Prognostic Value of Ki67 in Patients with Stage 1-2 Endometrial Cancer: Validation of the Cut-off Value of Ki67 as a Predictive Factor

Objective

The purpose of this study was to find a cut-off value of the immunohistochemical parameter Ki67 for stage I-II endometrial cancer.

Materials and Methods

The clinicopathological data of 318 patients with stages I-II endometrial cancer who received primary surgical treatment were retrospectively analyzed. A cut-off value of Ki67 for predicting recurrence of endometrial cancer was determined by using the receiver operating characteristic curve and the Youden index. The Cox regression was performed to screen factors associated with recurrence of endometrial cancer. Based on the cut-off value of Ki67, the patients were divided into two groups, and the differences of clinicopathological parameters between the two groups were compared.

Results

The receiver operating characteristic curve showed that the optimal cut-off value of Ki67 for predicting recurrence of patients with stages I-II endometrial cancer was 38%. The multivariate Cox regression analysis demonstrated that the histotypes (P=0.012), myometrial invasion (P=0.014), cervical stromal invasion (P=0.001), Ki67 (P=0.002), estrogen receptor (ER) (P=0.045) and P53 (P=0.032) were significant prognostic predictors for recurrence of endometrial cancer. The recurrence-free survival and the disease-specific survival of patients in the high-Ki67 group (Ki67 ≥38%) were much lower than those in the low-Ki67 group (Ki67 <38%) (P=0.000, P=0.001, respectively). Among the 118 patients with early low-risk endometrial cancer who did not receive adjuvant treatment after surgery, the recurrence-free survival of patients in the high-Ki67 group was also lower than those in the low-Ki67 group (P=0.000).

Conclusion

The Ki67 was demonstrated to be a useful prognostic factor in patients with stages I-II endometrial cancer, and the Ki67 labeling index 38.0% was optimal cut-off value for predicting recurrence.

Predicting Recurrence in Endometrial Cancer Based on a Combination of Classical Parameters and Immunohistochemical Markers

Objective

The aim of this study was to establish a nomogram to predict the recurrence of endometrial cancer (EC) by immunohistochemical markers and clinicopathological parameters and to evaluate the discriminative power of this model.

Methods

The data of 473 patients with stages I–III endometrial cancer who had received primary surgical treatment between October 2013 and May 2018 were randomly split into two sets: a training cohort and a validation cohort at a predefined ratio of 7:3. Univariate and multivariate Cox regression analysis of screening prognostic factors were performed in the training cohort (n=332) to develop a nomogram model for EC-recurrence prediction, which was further evaluated in the validation cohort (n=141).

Results

Univariate analysis found that FIGO stage, histological type, histological grade, myometrial invasion, cervical stromal invasion, postoperative adjuvant treatment, and four immunohistochemical markers (Ki67, ER, PR, and p53) were associated with recurrence in EC. Multivariate analysis showed that FIGO stage, histological type, ER, and p53 were superior parameters to generate the nomogram model for recurrence prediction in EC. Recurrence-free survival was better predicted by the proposed nomogram, with a C-index value of 0.79 (95% CI 0.66–0.92) in the validation cohort.

Conclusion

This nomogram model involving immunohistochemical markers can better predict recurrence in FIGO stages I–III EC.

Trends in hysterectomy-corrected uterine cancer mortality rates during 2002 to 2015: mortality of nonendometrioid cancer on the rise?

Corpus uteri cancer is the most common gynecological malignancy in most developed countries. The disease is typically diagnosed at an early stage, is of endometrioid histologic subtype, and has a fairly good prognosis. Here, we describe hysterectomy-corrected mortality rates of corpus uteri cancer, overall and stratified by age, stage and histologic subtype. Using data from nationwide Danish registries, we calculated uncorrected and hysterectomy-corrected age-standardized mortality rates of corpus uteri cancer among women ≥35 years during 2002 to 2015. Individual-level hysterectomy status was obtained from national registries; hysterectomy-corrected mortality rates were calculated by subtracting posthysterectomy person-years from the denominator, unless hysterectomy was performed due to corpus uteri cancer. Correction for hysterectomy resulted in a 25.5% higher mortality rate (12.3/100000 person-years vs 9.8/100000 person-years). Mortality rates were highest in women aged 70+, irrespective of year of death, histologic subtype and stage. A significant decline was observed in overall hysterectomy-corrected mortality rates from 2002 to 2015, particularly among women aged 70+. Mortality rates of endometrioid cancer declined significantly over time (annual percent change [APC]: -2.32, 95% CI -3.9, -0.7, P = .01), whereas rates of nonendometrioid cancer increased (APC: 5.90, 95% CI: 3.0, 8.9, P < .001). With respect to stage, mortality rates increased significantly over time for FIGOI-IIa (APC: 6.18 [95% CI: 1.9, 10.7] P = .01) but remained unchanged for FIGO IIb-IV. In conclusion, increasing mortality rates of nonendometrioid cancer paralleled the previously observed rise in incidence rates of this histologic subtype. Given the poor prognosis of nonendometrioid cancer, more studies are needed to clarify the underlying reason for these findings.

Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation

This study aimed at developing an available recurrence-free survival (RFS) model of endometrial cancer (EC) for accurate and individualized prognosis assessment. A training cohort of 520 women with EC who underwent initial surgical treatment and an external validation cohort of 445 eligible EC patients from 2006 to 2016 were analyzed retrospectively. Multivariable Cox proportional hazards regression models were used to develop nomograms for predicting recurrence. The concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were calculated to determine the discrimination of RFS prognostic scoring systems. Calibration plots were generated to examine the performance characteristics of the predictive nomograms. Regression analysis revealed that an advanced International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade 3, primary tumor diameter ≥2 cm, and positive peritoneal cytology were independent prognostic factors for RFS in EC in the training set. The nomograms estimated RFS according to these four variables, with a C-index of 0.860, which was superior to that of FIGO stage (2009 criteria), at 0.809 (P=0.034), in the training cohort. Encouragingly, consistent results were observed in the validation set, with a C-index of 0.875 for the nomogram and a C-index of 0.833 for the FIGO staging (P=0.0137). Furthermore, the calibrations of the nomograms predicting 3- and 5-year RFS strongly corresponded to the actual survival outcome. In conclusion, this study developed an available nomogram with effective external validation and relatively appreciable discrimination and conformity for the accurate assessment of 3- and 5-year RFS in Chinese women with EC.

Combination of preoperative neutrophil-lymphocyte ratio, platelet-lymphocyte ratio and monocyte-lymphocyte ratio: a superior prognostic factor of endometrial cancer

BACKGROUND

The preoperative peripheral blood neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and monocyte-lymphocyte ratio (MLR) have been reported to be associated with the prognosis of various cancers but are always discussed separately. The aim of this study is to bring the combination of NLR, PLR and MLR into the prognostic assessment system of endometrial cancer (EC) and establish a nomogram to provide an objective prediction model for clinical decisions.

METHODS

A total of 1111 patients with EC who had accepted surgical treatment during 2013-2017 were involved in the analysis. Their NLR, PLR, and MLR levels were obtained from a routine blood examination within 2 weeks before operation. Receiver operating characteristic curve (ROC) analysis was performed to determine optimal cutoffs. Chi-square tests analysed the associations of the ratios with other clinicopathological variables. The prognostic value was indicated by overall survival (OS) via Cox proportional hazards models and Kaplan-Meier analysis. R software was used to establish the nomogram based on the combination of NLR, PLR, MLR and other clinicopathological factors.

RESULTS

The median follow-up period was 40 months, and the median age was 56. The enrolled patients were stratified by cutoffs of 2.14 for NLR, 131.82 for PLR and 0.22 for MLR. Multivariate analyses demonstrated that high NLR over 2.14 (HR = 2.71, 95%CI = 1.83-4.02, P<0.001), high PLR over 131.82 (HR = 2.75, 95%CI = 1.90-3.97, P<0.001), and high MLR over 0.22 (HR = 1.72, 95%CI = 1.20-2.45, P = 0.003) were significantly associated with worse OS. The combined indicator, high NLR + high PLR + high MLR (HR = 4.34, 95%CI = 2.54-7.42, P<0.001), showed the highest prognostic value. The Harrell's concordance index of the nomogram was 0.847 (95% CI = 0.804-0.890), showing good discrimination and calibration of this model.

CONCLUSION

The combination of NLR, PLR, and MLR is a superior prognostic factor of EC. The nomogram involving the combination of NLR, PLR, MLR and other clinicopathological factors is recommended to predict OS for EC patients clinically.

Methylation‑associated silencing of miR‑638 promotes endometrial carcinoma progression by targeting MEF2C

Promoter methylation‑associated silencing of cancer‑associated microRNAs (miRNAs) is a common epigenetic mechanism during tumorigenesis in various types of human cancer. However, this has not been comprehensively examined in endometrial carcinoma (EC). In the present study, an miRNA microarray consisting of 1,347 common human miRNAs was used to select potential tumor suppressive miRNAs that were hyper‑methylated in EC. This led to the identification of miR‑638, miR‑210 and miR‑3665. The methylation status of miR‑638 was examined by bisulfite sequencing polymerase chain reaction and miR‑638 expression was measured by TaqMan miRNA assays. EC cell lines transfected with vectors overexpressing miR‑638, its target gene myocyte enhancer factor 2C (MEF2C) or both, were constructed. Dual‑luciferase reporter assays, a xenograft mouse model and rescue experiments were designed to study miR‑638 and its target gene MEF2C. The results indicated that the promoter region of miR‑638 was highly methylated and the expression of miR‑638 was significantly downregulated in cancerous tissues from 42 patients with EC who underwent surgical resection. Additionally, a low expression of miR‑638 was significantly associated with advanced Federation of Gynecology and Obstetrics stage and was demonstrated to indicate shorter disease‑free survival. Functional studies indicated that the overexpression of miR‑638 in EC cell lines inhibited in vitro tumor progression and in vivo tumorigenicity. MEF2C was verified as a direct target of miR‑638 and was demonstrated to mediate the tumor‑suppressive function of miR‑638 in EC.

Identification of a Multi-RNA-Type-Based Signature for Recurrence-Free Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the leading causes of death from gynecological cancer due to the high recurrence rate. A recent study indicated that molecular biomarkers can enhance the recurrence prediction power if they were integrated with clinical information. In this study, we attempted to identify a new multi-RNA-type-based molecular biomarker for predicting the recurrence risk and recurrence-free survival (RFS). Matched mRNA (including lncRNA) and miRNA RNA-sequencing data from 463 UCEC patients (n = 75, recurrent; n = 388, non-recurrent) were downloaded from The Cancer Genome Atlas database. LASSO (least absolute shrinkage and selection operator) analysis was used to screen the optimal combination of prognostic RNAs and then the risk score model was constructed. Moreover, the molecular mechanisms of prognostic RNAs were explored by establishing various interaction networks based on corresponding predictive databases. A multi-RNA-type-based signature (including three miRNAs: hsa-miR-6511b, hsa-miR-184, hsa-miR-4461; three lncRNAs: ENO1-IT1, MCCC1-AS1, AATBC; and 7 mRNAs: EPPK1, ASB9, BDNF, CYP11A1, ECEL1, EN2, F13A1) was developed for the prediction of RFS. The risk scoring system established by these signature genes was effective for the discrimination of the 5-year RFS in the high-risk from low-risk patients in the training [an area under the receiver operating characteristic curve (AUC) = 0.960], validation (AUC = 0.863), and entire datasets (AUC = 0.873). This risk score model was also proved to be a more excellent, independent prognostic discriminator than the single-RNA-type (overall AUC: 0.947 vs. 0.677, lncRNAs; 0.709, miRNAs; 0.899, mRNAs) and clinical staging (overall AUC: 0.947 vs. 0.517). Furthermore, the downstream mechanisms for some prognostic miRNAs or lncRNAs (HAND2-AS1-hsa-miR-6511b-APC2, PAX8-AS1-hsa-miR-4461-TNIK and MCCC1-AS1/ENO1-IT1-TNIK) were newly predicted based on the coexpression or competitive endogenous RNA theories. In conclusion, our findings may provide novel biomarkers for recurrence prediction and targets for treatment of UCEC.

Prognostic Factors and Nomograms to Predict Overall and Cancer-Specific Survival for Children with Wilms' Tumor

Objective

This study is aimed at constructing and verifying nomograms that forecast overall survival (OS) and cancer-specific survival (CSS) of children with Wilms' tumor (WT).

Patients and methods

Clinical information of 1613 WT patients who were under 18 years old between 1988 and 2010 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. Using these data, we performed univariate as well as multivariate Cox's regression analyses to determine independent prognostic factors for WT. Then, nomograms to predict 3- and 5-year OS and CSS rates were constructed based on the identified prognostic factors. The nomograms were validated externally and internally. The nomograms' reliability was evaluated utilizing receiver operating characteristic (ROC) curves and concordance indices (C-indices).

Results

1613 WT patients under 18 were involved in the study and randomly divided into the training (n = 1210) and validation (n = 403) cohorts. Age at diagnosis, tumor laterality, tumor size, tumor stage, and use of surgery were determined as independent prognostic factors for OS and CSS in WT and were further applied to construct prognostic nomograms. The C-index and area under the receiver operating characteristic curve (AUC) revealed the great performance of our nomograms. Internal and external calibration plots also showed excellent agreement between actual survival and nomogram prediction.

Conclusion

Precise and convenient nomograms were developed for forecasting OS and CSS of children with WT. These nomograms were able to offer accurate and individualized prognosis and assisted clinicians in performing suitable therapy.

Prognostic Potential of Alternative Splicing Markers in Endometrial Cancer

Alternative splicing (AS), an important post-transcriptional regulatory mechanism that regulates the translation of mRNA isoforms and generates protein diversity, has been widely demonstrated to be associated with oncogenic processes. In this study, we systematically analyzed genome-wide AS patterns to explore the prognostic implications of AS in endometrial cancer (EC). A total of 2,324 AS events were identified as being associated with the overall survival of EC patients, and eleven of these events were further selected using a random forest algorithm. With the implementation of a generalized, boosted regression model, a prognostic AS model that aggregated these eleven markers was ultimately established with high performance for risk stratification in EC patients. Functional analysis of these eleven AS markers revealed various potential signaling pathways implicated in the progression of EC. Splicing network analysis demonstrated the notable correlation between the expression of splicing factors and AS markers in EC and further determined eight candidate splicing factors that could be therapeutic targets for EC. Taken together, the results of this study present the utility of AS profiling in identifying biomarkers for the prognosis of EC and provide comprehensive insight into the molecular mechanisms involved in EC processes.

Investigating Novel Genes Potentially Involved in Endometrial Adenocarcinoma using Next-Generation Sequencing and Bioinformatic Approaches

Endometrial cancer is one of the most common cancers in women worldwide, affecting more than 300,000 women annually. Dysregulated gene expression, especially those mediated by microRNAs, play important role in the development and progression of cancer. This study aimed to investigate differentially expressed genes in endometrial adenocarcinoma using next generation sequencing (NGS) and bioinformatics. The gene expression profiles and microRNA profiles of endometrial adenocarcinoma (cancer part) and normal endometrial tissue (non-cancer part) were assessed with NGS. We identified 56 significantly dysregulated genes, including 47 upregulated and 9 downregulated genes, in endometrial adenocarcinoma. Most of these genes were associated with defense response, response to stimulus, and immune system process, and further pathway analysis showed that human papillomavirus infection was the most significant pathway in endometrial adenocarcinoma. In addition, these genes were also associated with decreased cell death and survival as well as increased cellular movement. The analyses using Human Protein Atlas, identified 6 genes (PEG10, CLDN1, ASS1, WNT7A, GLDC, and RSAD2) significantly associated with poorer prognosis and 3 genes (SFN, PIGR, and CDKN1A) significantly associated with better prognosis. Combining with the data of microRNA profiles using microRNA target predicting tools, two significantly dysregulated microRNA-mediated gene expression changes in endometrial adenocarcinoma were identified: downregulated hsa-miR-127-5p with upregulated CSTB and upregulated hsa-miR-218-5p with downregulated HPGD. These findings may contribute important new insights into possible novel diagnostic or therapeutic strategies for endometrial adenocarcinoma.

Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest

Traditional clinical features are not sufficient to accurately judge the prognosis of endometrioid endometrial adenocarcinoma (EEA). Molecular biological characteristics and traditional clinical features are particularly important in the prognosis of EEA. The aim of the present study was to establish a predictive model that considers genes and clinical features for the prognosis of EEA. The clinical and RNA sequencing expression data of EEA were derived from samples from The Cancer Genome Atlas (TCGA) and Peking University People's Hospital (PKUPH; Beijing, China). Samples from TCGA were used as the training set, and samples from the PKUPH were used as the testing set. Variable selection using Random Forests (VSURF) was used to select the genes and clinical features on the basis of TCGA samples. The RF classification method was used to establish the prediction model. Kaplan-Meier curves were tested with the log-rank test. The results from this study demonstrated that on the basis of TCGA samples, 11 genes and the grade were selected as the input features. In the training set, the out-of-bag (OOB) error of RF model-1, which was established using the '11 genes', was 0.15; the OOB error of RF model-2, which was established using the 'grade', was 0.39; and the OOB error of RF model-3, established using the '11 genes and grade', was 0.15. In the testing set, the classification accuracy of RF model-1, model-2 and model-3 was 71.43, 66.67 and 80.95%, respectively. In conclusion, to the best of our knowledge, the VSURF was used to select features relevant to EEA prognosis, and an EEA predictive model combining genes and traditional features was established for the first time in the present study. The prediction accuracy of the RF model on the basis of the 11 genes and grade was markedly higher than that of the RF models established by either the 11 genes or grade alone.

Impact of vaginal brachytherapy in intermediate and high-intermediate risk endometrial cancer: a multicenter study from the FRANCOGYN group

Objective

According to recent European Society of Medical Oncology, European Society of Gynaecological Oncology and European Society of Radiotherapy and Oncology guidelines, adjuvant vaginal brachytherapy (VB) is optional in patients with intermediate risk (IR) and high-intermediate risk (HIR) endometrial cancer (EC). The aim of this French retrospective, multicenter study was to assess the impact of VB in these groups on local recurrence rate, local recurrence-free survival (RFS) and overall survival (OS).

Methods

Data of 191 patients with IR and HIR EC who underwent primary surgery with or without VB and no other adjuvant treatment between 2000 and 2016 were extracted from the FRANCOGYN database. Rate of local recurrence, OS and local RFS in these two groups were compared using the Kaplan-Meier method.

Results

The number of patients with IR and HIR EC were 118 and 73 respectively. VB was used in 92 patients in IR group and 43 in HIR group. Median follow-up was 22 months. In the HIR group, the local recurrence rate was significantly higher in the no adjuvant therapy group in comparison with the VB group (16.7% and 0% respectively, p=0.02). There was also a significant improvement in local RFS (p=0.01) in VB group. In IR EC, there is no significant difference on local recurrence rate (4.2% and 3.2%, respectively, p=1.00) or local RFS (p=0.54) between the two groups.

Conclusions

VB is an efficient adjuvant treatment for patients with HIR EC. VB is not associated with an improvement of RFS or OS in IR EC patient.

Impact of Coexisting Uterine Adenomyosis on the Survival Outcome of Patients with Endometrial Cancer: A Retrospective Cohort Study

Objective:

To determine the effects of uterine adenomyosis on endometrial cancerrecurrence rates.

Methods:

This retrospective cohort study reviewed all consecutive patients diagnosed with endometrial cancerwho underwent total hysterectomy-based surgical staging at Srinagarind Hospital between January, 2010 and January, 2016. The patientswere divided into two groups:a uterine adenomyosisgroup and a non-adenomyosis group. Patient demographics, type of surgery, histopathology, stage of endometrial cancer, adjuvant treatment, and survival outcomes were compared.

Results:

A total 350 patients were enrolled, with 132 (37.71%) in the adenomyosis group and 218 (62.29%) in the non-adenomyosis group. Deep myometrial invasion and lymphovascular space invasion (LVSI) were more commonly found among patients who had no adenomyosis compared to those with adenomyosis(52.8% vs 39.4%, P=0.02 and 53.2% vs. 38.6%, P=0.01). There were no significant differences in terms of five-year recurrence-free survival (HR=1.47; 95%CI 0.88-2.44) and five-year overall survival (HR=0.81; 95%CI 0.43-1.53) between the two comparison groups.

Conclusion:

Coexisting uterine adenomyosis in endometrial cancer wasassociated withdeep myometrial invasion and LVSI but did not have significant impact on survival.

Role of less commonly agreed risk factors on disease recurrence in endometrial cancer: a propensity scorematched comparison

Objective:

To compare the clinicopathologic features of patients with endometrial cancer (EC) with recurrent disease with a primary surgery, stage, grade, and tumor histotype-matched cohort of patients without recurrence.

Materials and Methods:

Patients with EC who were surgically treated at a single tertiary care institution between 2005 and 2015 were enrolled in this study. The dataset included 381 consecutive patients with EC, of which 31 (8.1%) had disease recurrence. Data consisting of age at surgery, CA- 125 concentration at diagnosis, number of lymph nodes harvested, growth pattern of the primary tumor, location of the primary tumor within the endometrium, tumor histotype, tumor grade, disease stage, adjuvant therapy, disease recurrence, time to recurrence, CA-125 concentration at recurrence, clinical and imaging findings at recurrence, and treatment modalities used for recurrent disease were extracted from the institutional database.

Results:

After 1-to-1 propensity-score matching of patients with and without recurrence, the clinicopathologic features of 26 patients from each group were compared. Patients with recurrent disease were found to have a significantly higher CA-125 concentration at initial diagnosis (p<0.001) and different tumor growth pattern (p=0.019) than patients without disease recurrence. The papillary growth pattern of the primary tumor was significantly associated with disease recurrence as compared with polypoid and infiltrative patterns. Omental involvement, papillary tumor growth, and advanced age were associated with increased mortality.

Conclusion:

Our results indicated that higher CA-125 concentrations at initial diagnosis and papillary growth pattern of primary tumors were found to be associated with disease recurrence.

A Simplified Risk Stratification Method for Women With Stage I Endometrial Carcinoma

OBJECTIVES

Available risk stratification methods for women with endometrial carcinoma are controversially defined. We sought to develop a simplified and an individualized prognostic index for cancer recurrence in women with International Federation of Gynecology and Obstetrics (FIGO) stage I endometrial carcinoma, solely of endometrioid histology.

MATERIALS AND METHODS

We identified 976 women who underwent a hysterectomy and did not receive any adjuvant therapy. Cox proportional hazards model was used to identify independent predictors of recurrence. Prognostic groups were created based on the number of independent predictors of recurrence (0, 1, or 2 or 3 risk factors). These groups were then validated using a separate cohort of 611 women treated at another academic institution. The model's performance for predicting cancer recurrence was measured by the concordance probability estimate along with a 95% confidence interval.

RESULTS

Median follow-up was 65 months. The final recurrence model included 3 risk groups based on 3 independent predictors of recurrence (tumor grade 2 or 3, the presence of lymphovascular space invasion and stage IB). Five-year recurrence rates were 4%, 16%, and 44% for groups 0, 1, and 2 or 3, respectively. The performance of the model was very good with a concordance probability estimate of 0.72 and 0.80 for the development and validation cohorts, respectively.

CONCLUSIONS

On the basis of 3 well-known prognostic factors, we have developed and externally validated a simplified prognostic model that accurately predicts cancer recurrence in women with stage I endometrial carcinoma. This simplified predictive tool may be helpful in estimating individualized risk of recurrence and guide counseling with regard to adjuvant treatment.

The Memorial Sloan Kettering Cancer Center Nomogram is More Accurate than the 2009 FIGO Staging System in the Prediction of Overall Survival in a German Endometrial Cancer Patient Cohort

BACKGROUND

Despite the complexity of endometrial cancer (EC) tumor biology, treatment decisions are still mainly based on the post-surgical International Federation of Gynecology and Obstetrics (FIGO) stage. Prediction models considering more prognostic factors may represent a better risk assessment than FIGO stage alone. We tested the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram for the prediction of overall survival (OS) in a German EC population.

METHODS

Overall, 454 EC patients (322 type I and 132 type II) who received primary surgical treatment at our department between 1991 and 2011 were included in the analysis with a dataset of 68 covariates. Predicted OS was calculated using the online MSKCC nomogram and compared with the observed survival in our population. To estimate the discriminatory power, the concordance probabilities were calculated using the concordance probability estimate (CPE). Receiver operating characteristic curves were created and the area under the curve (AUC) values compared between predicted and actual OS.

RESULTS

After a mean follow-up of 183 months, 211 patients were reported dead (47%). Mean OS for all stages was 101 months (standard deviation 66.7 months). The 2009 FIGO system showed an AUC value of 0.6 and a CPE of 0.63, while the 3-year OS prediction of the MSKCC nomogram showed an AUC value of 0.8 and a CPE of 0.77.

CONCLUSION

This external validation of the MSKCC nomogram showed better discrimination and calibration values than the conventional FIGO classification system. The nomogram was externally validated and can serve as a tool for better risk-adapted treatment decisions and patient stratification, e.g. in clinical trials.

A Proposal for a Classification for Recurrent Endometrial Cancer: Analysis of a French Multicenter Database From the FRANCOGYN Study Group

OBJECTIVE

Endometrial cancer (EC) recurrences are relatively common with no standardized way of describing them. We propose a new classification for them called locoregional, nodal, metastasis, carcinomatosis recurrences (rLMNC).

PATIENTS AND METHODS

The data of 1230 women with EC who were initially treated by primary surgery were included in this French multicenter retrospective study. Recurrences were classified based on dissemination pathways: (1) locoregional recurrence (rL); (2) nodal recurrence (rN) for lymphatic pathway; (3) distant organ recurrence (rM) for hematogenous pathway; and (4) carcinomatosis recurrence (rC) for peritoneal pathway. These pathways were further divided into subgroups. We compared recurrence free survival and overall survival (OS) between the 4 groups (rL/rN/rM/rC).

RESULTS

The median follow-up was 35.6 months (range, 1.70-167.60). One hundred ninety-eight women (18.2%) experienced a recurrence: 150 (75.8%) experienced a single-pathway recurrence and 48 (24.2%) a multiple-pathway recurrence. The 5-year OS was 34.1% (95% confidence interval [CI], 27.02%-43.1%), and the median time to first recurrence was 18.9 months (range, 0-152 months). The median survival after recurrence was 14.8 months (95% CI, 11.7-18.8). Among women with single pathway of recurrence, a difference in 5-year OS (P < 0.001) and survival after recurrence (P < 0.01) was found between the 4 rLNMC groups. The carcinomatosis group had the worst prognosis compared with other single recurrence pathways. Women with multiple recurrences had poorer 5-year OS (P < 0.001) and survival after recurrence (P < 0.01) than those with single metastasis recurrence, other than women with peritoneal carcinomatosis.

CONCLUSIONS

This easy-to-use and intuitive classification may be helpful to define EC recurrence risk groups and develop guidelines for the management of recurrence. Its prognosis value could also be a tool to select homogenous populations for further trials.

Identification of micro-RNA expression profile related to recurrence in women with ESMO low-risk endometrial cancer

Background

Actual European pathological classification of early-stage endometrial cancer (EC) may show insufficient accuracy to precisely stratify recurrence risk, leading to potential over or under treatment. Micro-RNAs are post-transcriptional regulators involved in carcinogenic mechanisms, with some micro-RNA patterns of expression associated with EC characteristics and prognosis. We previously demonstrated that downregulation of micro-RNA-184 was associated with lymph node involvement in low-risk EC (LREC). The aim of this study was to evaluate whether micro-RNA signature in tumor tissues from LREC women can be correlated with the occurrence of recurrences.

Methods

MicroRNA expression was assessed by chip analysis and qRT-PCR in 7 formalin-fixed paraffin-embedded (FFPE) LREC primary tumors from women whose follow up showed recurrences (R+) and in 14 FFPE LREC primary tumors from women whose follow up did not show any recurrence (R−), matched for grade and age. Various statistical analyses, including enrichment analysis and a minimum p-value approach, were performed.

Results

The expression levels of micro-RNAs-184, -497-5p, and -196b-3p were significantly lower in R+ compared to R− women. Women with a micro-RNA-184 fold change < 0.083 were more likely to show recurrence (n = 6; 66%) compared to those with a micro-RNA-184 fold change > 0.083 (n = 1; 8%), p = 0.016. Women with a micro-RNA-196 fold change < 0.56 were more likely to show recurrence (n = 5; 100%) compared to those with a micro-RNA-196 fold change > 0.56 (n = 2; 13%), p = 0.001.

Conclusions

These findings confirm the great interest of micro-RNA-184 as a prognostic tool to improve the management of LREC women.

[Primary management of endometrial carcinoma. Joint recommendations of the French society of gynecologic oncology (SFOG) and of the French college of obstetricians and gynecologists (CNGOF)]

OBJECTIVES

The management of endometrial carcinoma is constantly evolving. The SFOG and the CNGOF decided to jointly update the previous French recommendations (Institut national du cancer 2011) and to adapt to the French practice the 2015 recommendations elaborated at the time of joint European consensus conference with the participation of the three concerned European societies (ESGO, ESTRO, ESMO).

MATERIAL AND METHODS

A strict methodology was used. A steering committee was put together. A systematic review of the literature since 2011 has been carried out. A first draft of the recommendations has been elaborated, with emphasis on high level of evidence. An external review by users representing all the concerned discipines and all kinds of practice was completed. Three hundred and four comments were sent by 54 reviewers.

RESULTS

The management of endometrial carcinoma requires a precise preoperative workup. A provisional estimate of the final stage is provided. This estimation impact the level of surgical staging. Surgery should use a minimal invasive approach. The final pathology is the key of the decision concerning adjuvant therapy, which involves surveillance, radiation therapy, brachytherapy, or chemotherapy.

CONCLUSION

The management algorithms allow a fast, state of the art based, answer to the clinical questions raised by the management of endometrial cancer. They must be used only in the setting of a multidisciplinary team at all stages of the management.