Review the progression of ovarian clear cell carcinoma from the perspective of genomics and epigenomics

Somatic mutation of targeted sequencing identifies risk stratification in advanced ovarian clear cell carcinoma

BACKGROUND

Ovarian clear cell carcinoma (OCCC) is a unique subtype of epithelial ovarian cancer. Advanced OCCC display a poor prognosis. Therefore, we aimed to make risk stratification for precise medicine.

METHODS

We performed a large next generation sequencing (NGS) gene panel on 44 patients with OCCC in FIGO stage II-IV. Then, by machine learning algorithms, including extreme gradient boosting (XGBoost), random survival forest (RSF), and Cox regression, we screened for feature genes associated with prognosis and constructed a 5-gene panel for risk stratification. The prediction efficacy of the 5-gene panel was compared with FIGO stage and residual disease by receiver operating characteristic curve and decision curve analysis.

RESULTS

The feature mutated genes related to prognosis, selected by machine learning algorithms, include MUC16, ATM, NOTCH3, KMT2A, and CTNNA1. The 5-gene panel can effectively distinguish the prognosis, as well as platinum response, of advanced OCCC in both internal and external cohorts, with the predictive capability superior to FIGO stage and residual disease.

CONCLUSIONS

Mutations in genes, including MUC16, ATM, NOTCH3, KMT2A, and CTNNA1, were associated with the poor prognosis of advanced OCCC. The risk stratification according to these genes demonstrated acceptable prediction power of prognosis and platinum response, suggesting the potential to be a novel target for precision medicine.

Spatial tumor immune microenvironment phenotypes in ovarian cancer

Immunotherapy has largely failed in ovarian carcinoma (OC), likely due to that the vast tumor heterogeneity and variation in immune response have hampered clinical trial outcomes. Tumor-immune microenvironment (TIME) profiling may aid in stratification of OC tumors for guiding treatment selection. Here, we used Digital Spatial Profiling combined with image analysis to characterize regions of spatially distinct TIME phenotypes in OC to assess whether immune infiltration pattern can predict presence of immuno-oncology targets. Tumors with diffuse immune infiltration and increased tumor-immune spatial interactions had higher presence of IDO1, PD-L1, PD-1 and Tim-3, while focal immune niches had more CD163 macrophages and a preliminary worse outcome. Immune exclusion was associated with presence of Tregs and Fibronectin. High-grade serous OC showed an overall stronger immune response and presence of multiple targetable checkpoints. Low-grade serous OC was associated with diffuse infiltration and a high expression of STING, while endometrioid OC had higher presence of CTLA-4. Mucinous and clear cell OC were dominated by focal immune clusters and immune-excluded regions, with mucinous tumors displaying T-cell rich immune niches.

Histologic Subtypes in Endometriosis-Associated Ovarian Cancer and Ovarian Cancer Arising in Endometriosis: A Systematic Review and Meta-Analysis

The definition of the association between ovarian cancer and endometriosis was first reported by Sampson in 1925. He identified the following criteria: (a) clear evidence of endometriosis in proximity to the tumour, (b) exclusion of a metastatic tumour to the ovary, (c) presence of tissue resembling endometrial stroma surrounding epithelial glands. The naming of these cancers is "endometriosis-associated ovarian cancer" (EAOC). Scott proposed an additional stringent criterion: evidence of histological transition from endometriosis to cancer is to define "ovarian cancer arising in endometriosis" (OCAE). The aim of this systematic review is to analyse the distribution of different ovarian cancer histotypes in EAOC and OCAE to understand their similarities and differences. A total of 31 studies were included. Four studies added data for both EAOC and OCAE. Twenty-three studies were selected for EAOC, with a total of 800 patients, and 12 studies were selected for OCAE, with a total of 375 patients. The results show no significant differences in the distribution of histotypes in the two populations analysed. Clear cell carcinoma (CCC) and endometrioid carcinoma (EC) were the most common subtypes and were less frequent in EAOC compared to OCAE; the odd ratios were 0.58 (0.26-1.29) and 0.65 (0.33-1.26) respectively, although the difference was not statistically significant. The other histotypes were present in small proportions. This analysis shows that the histological profiles of EAOC and OCAE are similar, suggesting a similar aetiopathological mechanism, which requires further research to investigate whether EAOC and OCAE may be in the same way but at different points of the process to malignancy or have different pathways of progression to malignancy.

Fusobacterium nucleatum: An Overview of Evidence, Demi-Decadal Trends, and Its Role in Adverse Pregnancy Outcomes and Various Gynecological Diseases, including Cancers

Gynecological and obstetric infectious diseases are crucial to women's health. There is growing evidence that links the presence of Fusobacterium nucleatum (F. nucleatum), an anaerobic oral commensal and potential periodontal pathogen, to the development and progression of various human diseases, including cancers. While the role of this opportunistic oral pathogen has been extensively studied in colorectal cancer in recent years, research on its epidemiological evidence and mechanistic link to gynecological diseases (GDs) is still ongoing. Thus, the present review, which is the first of its kind, aims to undertake a comprehensive and critical reappraisal of F. nucleatum, including the genetics and mechanistic role in promoting adverse pregnancy outcomes (APOs) and various GDs, including cancers. Additionally, this review discusses new conceptual advances that link the immunomodulatory role of F. nucleatum to the development and progression of breast, ovarian, endometrial, and cervical carcinomas through the activation of various direct and indirect signaling pathways. However, further studies are needed to explore and elucidate the highly dynamic process of host-F. nucleatum interactions and discover new pathways, which will pave the way for the development of better preventive and therapeutic strategies against this pathobiont.

Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma

BACKGROUND

Ovarian clear cell carcinoma (OCCC) represents a subtype of ovarian epithelial carcinoma (OEC) known for its limited responsiveness to chemotherapy, and the onset of distant metastasis significantly impacts patient prognoses. This study aimed to identify potential risk factors contributing to the occurrence of distant metastasis in OCCC.

METHODS

Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients diagnosed with OCCC between 2004 and 2015. The most influential factors were selected through the application of Gaussian Naive Bayes (GNB) and Adaboost machine learning algorithms, employing a Venn test for further refinement. Subsequently, six machine learning (ML) techniques, namely XGBoost, LightGBM, Random Forest (RF), Adaptive Boosting (Adaboost), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were employed to construct predictive models for distant metastasis. Shapley Additive Interpretation (SHAP) analysis facilitated a visual interpretation for individual patient. Model validity was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the receiver operating characteristic curve (AUC).

RESULTS

In the realm of predicting distant metastasis, the Random Forest (RF) model outperformed the other five machine learning algorithms. The RF model demonstrated accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) values of 0.792 (0.762-0.823), 0.904 (0.835-0.973), 0.759 (0.731-0.787), 0.221 (0.186-0.256), 0.974 (0.967-0.982), 0.353 (0.306-0.399), and 0.834 (0.696-0.967), respectively, surpassing the performance of other models. Additionally, the calibration curve's Brier Score (95%) for the RF model reached the minimum value of 0.06256 (0.05753-0.06759). SHAP analysis provided independent explanations, reaffirming the critical clinical factors associated with the risk of metastasis in OCCC patients.

CONCLUSIONS

This study successfully established a precise predictive model for OCCC patient metastasis using machine learning techniques, offering valuable support to clinicians in making informed clinical decisions.