Novel approaches for treatment of endometrial carcinoma

XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis

Endometrial carcinoma's (EC) etiology is complex and involves DNA repair gene polymorphisms like XRCC1-Arg399Gln and hOGG1-Ser326Cys, but their association with the disease is unclear. Following PRISMA, we conducted a systematic review and meta-analysis, collecting data from four databases. The studies needed to be population-based case-control studies examining the association between the named polymorphisms and EC. Quality was assessed with the Newcastle-Ottawa Scale. Pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated, and subgroup analyses were conducted based on ethnicity. Seven studies were included. Both polymorphisms were found to significantly increase EC risk, particularly in Caucasians. XRCC1-Arg399Gln showed a dominant model OR of 1.14 (95% CI: 1.01-1.29) and a homozygous model OR of 1.59 (95% CI: 1.12-2.25). The heterozygote model OR for hOGG1-Ser326Cys was 1.29 (95% CI: 1.02-1.63), and the allele OR was 1.31 (95% CI: 1.07-1.60). XRCC1-Arg399Gln and hOGG1-Ser326Cys may increase EC risk, primarily in Caucasian women, emphasizing the role of DNA repair in disease susceptibility. More extensive studies are needed to validate these findings in diverse ethnicities and investigate other DNA repair gene polymorphisms.

Number of Positive Lymph Nodes and Survival in Endometrial Carcinoma: A Proposal for a Modified Staging

Purpose

To construct a new clinical staging system including the number of lymph node metastases to supplement the International Federation of Gynecology and Obstetrics (FIGO) staging for the prognosis of endometrial carcinoma patients.

Methods

This cohort study retrieved the data of 28,824 patients confirmed as endometrial carcinoma between 2010 and 2015 in the surveillance, epidemiology, and end results (SEER) database. COX risk proportional model was established to evaluate the association between FIGO staging with the all-cause mortality of endometrial carcinoma. The diagnostic value of FIGO staging and the new staging for the mortality of patients were evaluated by receiver operator characteristic curve (ROC). Hazard ratio (HR) and 95% confidence interval (CI) were effect size.

Results

The 5-year survival rate of all participants was 77.21%. The median follow-up time was 60.00 (60.00,60.00) months. Patients at FIGO staging IB (HR=1.75, 95% CI: 1.62-1.90), FIGO staging II (HR=2.22, 95% CI: 2.00-2.47), FIGO staging IIIA (HR=2.74, 95% CI: 2.43-3.09), FIGO staging IIIB (HR=4.07, 95% CI: 3.48-4.76), FIGO staging IIIC1 (HR=3.84, 95% CI: 3.52-4.20), FIGO staging IIIC2 (HR=4.52, 95% CI: 4.09-4.99), FIGO staging IVA (HR=5.56, 95% CI: 4.58-6.74), and FIGO staging IVB (HR=7.62, 95% CI: 6.94-8.36) were associated with increased risk of all-cause mortality of endometrial carcinoma patients. After adding positive lymph nodes as another covariate in Model 3, the effect on of FIGO staging survival was reduced when the FIGO staging was higher than stage III/IV. The C-index of the new staging 0.781 (95% CI: 0.774-0.787) was higher than FIGO staging 0.776 (95% CI: 0.770-0.783).

Conclusion

Our new staging using the number of positive lymph nodes supplement to the FIGO staging was superior than the FIGO staging for predicting the prognosis of endometrial cancer patients, which might help more accurately identify endometrial carcinoma patients who were at high risk of mortality and offer timely treatments in these patients.

Integrative models of histopathological images and multi-omics data predict prognosis in endometrial carcinoma

Objective

This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging.

Methods

The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set.

Results

Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathology and omics transcends that of genomics, transcriptomics, or proteomics alone. Additionally, multi-dimensional omics data, including HIF, genomics, transcriptomics, and proteomics, attained the largest AUCs of 0.866, 0.869, and 0.856 at years 1, 3, and 5, respectively, showcasing the highest discrepancy in survival (HR = 18.347, 95% CI [11.09-25.65], p < 0.001).

Conclusions

The results of this experiment indicated that the complementary features of HIF could improve the prognostic performance of EC patients. Moreover, the integration of HIF and multi-dimensional omics data might ameliorate survival prediction and risk stratification in clinical practice.

Do Not Forget about Hormonal Therapy for Recurrent Endometrial Cancer: A Review of Options, Updates, and New Combinations

Hormonal therapy has long been a treatment modality for recurrent endometrial cancer. It is appealing for patients with low-grade, slow-growing tumors or in patients for which other treatment types may be too toxic. Hormonal therapy is well tolerated and has response rates ranging from 9 to 33%. Hormonal treatment options take advantage of the estrogen-dependent molecular pathways in endometrial cancers. Current options for hormonal therapies include progesterone therapy (medroxyprogesterone acetate and megestrol acetate) as a single agent or in combination and agents that target the estrogen pathway. Aromatase inhibitors have had modest single-agent activity, but synergistic effects have been found when used in combination with targeted therapy including mTOR inhibitors and cyclin-dependent kinase 4/6 (CDK4/6) inhibitors. Molecular profiling of endometrial cancers has begun to help individualize treatments. This review will report on existing data and ongoing trials investigating novel hormonal therapy agents.

Analysis of prognostic factors of metastatic endometrial cancer based on surveillance, epidemiology, and end results database

Objective

To explore the risk factors for survival and prognosis of patients with metastatic endometrial cancer and to build and verify a reliable prediction model.

Methods

We retrospectively analyzed patients diagnosed with metastatic endometrial cancer in the US Surveillance, Epidemiology, and End Results (SEER) database between January 2010 and December 2015. Univariate and multivariate Cox regression analyses were used to assess clinical variables impact on survival and to construct nomograms. The results of the consistency index (C-index), subject operating characteristic (ROC) curve, and calibration curve were used to evaluate the predictive ability of the nomogram.

Results

This study included 3,878 patients with metastatic endometrial cancer. In the univariate analysis, variables associated with overall survival (OS) and cancer-specific survival (CSS) included age, race, marital status, pathological type, pathological grade, T-stage, N-stage, surgery, radiotherapy, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. In the multivariate analysis, age, race, pathological type, pathological grade, T-stage, N-stage, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, and lung metastasis were independent risk factors for OS and CSS (all P < 0.05). Combined with the results of the multiple factors, the 1-, 3-, 5-, and 8-year nomograms were constructed. For OS and CSS, T-stage had the greatest impact on the adverse prognosis of patients with metastatic endometrial cancer. The C-indexes of the OS and CSS nomograms in the training cohort were 0.749 (95% CI, 0.739-0.760) and 0.746 (95% CI, 0.736-0.756), respectively. The C-indices of OS and CSS in the validation cohort were 0.730 (95% CI, 0.714-0.746) and 0.728 (95% CI, 0.712-0.744), respectively. The ROC curve revealed our model's good prediction accuracy and clinical practicability. The calibration curve also confirmed the consistency between the model and actual existence. The Kaplan-Meier curves revealed statistically significant differences between the risk subgroups (P < 0.05).

Conclusion

Our SEER-based nomograms for predicting survival in patients with metastatic endometrial cancer were helpful for the clinical evaluation of patient prognosis.