(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.

Despite treatment advances in newly diagnosed advanced-stage ovarian cancer (aOC), improved outcomes are needed. DUO-O (NCT03737643), a phase III placebo-controlled trial, enrolled patients with newly diagnosed aOC. Following one cycle of carboplatin/paclitaxel ± bevacizumab, patients without a tumor BRCA mutation (non-tBRCAm) were randomly assigned (1 : 1 : 1) at cycle 2 to carboplatin/paclitaxel plus bevacizumab followed by bevacizumab (control); carboplatin/paclitaxel, bevacizumab plus durvalumab followed by bevacizumab plus durvalumab (durvalumab arm); or carboplatin/paclitaxel, bevacizumab plus durvalumab followed by bevacizumab, durvalumab plus olaparib (durvalumab + olaparib arm). Investigator-assessed progression-free survival (PFS; primary endpoint) was tested for the durvalumab + olaparib arm versus control in the non-tBRCAm homologous recombination deficiency (HRD)-positive and non-tBRCAm intention-to-treat (ITT) populations. One thousand one hundred and thirty patients were randomly allocated to the study. The prespecified interim PFS analysis [data cut-off (DCO): 5 December 2022] qualified as the primary analysis; PFS hazard ratio (HR) for the durvalumab + olaparib arm versus control was 0.49 [95% confidence interval (CI) 0.34-0.69, P < 0.0001; median (m) PFS 37.3 versus 23.0 months] in the non-tBRCAm HRD-positive and 0.63 (95% CI 0.52-0.76, P < 0.0001; mPFS 24.2 versus 19.3 months) in the non-tBRCAm ITT population. For the durvalumab arm versus control, PFS HR was 0.87 (95% CI 0.73-1.04, P = 0.13; mPFS 20.6 versus 19.3 months) in the non-tBRCAm ITT population. At final PFS and interim overall survival (OS) analysis (DCO: 18 September 2023), PFS results were consistent with primary analysis; interim OS HR for the durvalumab + olaparib arm versus control was 0.95 (95% CI 0.76-1.20, P = 0.68; 39.0% maturity) in the non-tBRCAm ITT population. Safety was generally consistent with the profiles of the individual agents. DUO-O met its primary PFS endpoints for first-line durvalumab plus carboplatin/paclitaxel and bevacizumab followed by durvalumab, bevacizumab plus olaparib maintenance versus carboplatin/paclitaxel and bevacizumab followed by bevacizumab in the non-tBRCAm HRD-positive and non-tBRCAm ITT populations. Further insight into long-term benefit is anticipated with additional follow-up.

Histone H3 lysine 4 trimethylation (H3K4me3) is one of the most recognized epigenetic regulators of transcriptional activity representing, an epigenetic modification of Histone H3. Previous reports have suggested that the broad H3K4me3 domain can be considered as an epigenetic signature for tumor-suppressor genes in human cells. G-protein-coupled estrogen receptor (GPER), a new membrane-bound estrogen receptor, acts as an inhibitor on cell growth via epigenetic regulation in breast and ovarian cancer cells. This study was conducted to evaluate the relationship of GPER and H3K4me3 in ovarian cancer tissue samples as well as in two different cell lines (Caov3 and Caov4). Silencing of GPER by a specific siRNA and two selective regulators with agonistic (G1) and antagonistic (G15) activity were applied for consecutive in vitro studies to investigate their impacts on tumor cell growth and the changes in phosphorylated ERK1/2 (p-ERK1/2) and H3K4me3. We found a positive correlation between GPER and H3K4me3 expression in ovarian cancer patients. Patients overexpressing GPER as well as H3K4me3 had significantly improved overall survival. Increased H3K4me3 and p-ERK1/2 levels and attenuated cell proliferation and migration were observed in Caov3 and Caov4 cells via activation of GPER by G1. Conversely, antagonizing GPER activity by G15 resulted in opposite effects in the Caov4 cell line. In conclusion, interaction of GPER and H3K4me3 appears to be of prognostic significance for ovarian cancer patients. The results of the in vitro analyses confirm the biological rationale for their interplay and identify GPER agonists, such as G1, as a potential therapeutic approach for future investigations.

Multi drug resistance protein 1 (MDR1) expression on tumor cells has been widely investigated in context of drug resistance. However, the role of MDR1 on the immune cell infiltrate of solid tumors remains unknown. The aim of this study was to analyze the prognostic significance of a MDR1+ immune cell infiltrate in epithelial ovarian cancer (EOC) and to identify the MDR1+ leucocyte subpopulation. MDR1 expression was analyzed by immunohistochemistry in 156 EOC samples. In addition to MDR1+ cancer cells, we detected a MDR1+ leucocyte infiltrate (high infiltrate >4 leucocytes per field of view). Correlations and survival analyses were calculated. To identify immune cell subpopulations immunofluorescence double staining was performed. The MDR1+ leucocyte infiltrate was associated with human epidermal growth factor receptor 2 (HER2) (cc = 0.258, p = 0.005) and tumor-associated mucin 1 (TA-MUC1) (cc = 0.202, p = 0.022) expression on cancer cells. A high MDR1+ leucocyte infiltrate was associated with impaired survival, especially in patients whose carcinoma showed either serous histology (median OS 28.80 vs. 50.64 months, p = 0.027, n = 91) or TA-MUC1 expression (median OS 30.60 vs. 63.36 months, p = 0.015, n = 110). Similar findings for PFS suggest an influence of MDR1+ immune cells on the development of chemoresistance. A Cox regression analysis confirmed the independency of a high MDR1+ leucocyte infiltrate as prognostic factor. M2 macrophages were identified as main part of the MDR1+ leucocyte infiltrate expressing MDR1 as well as the M2 marker CD163 and the pan-macrophage marker CD68. Infiltration of MDR1+ leucocytes, mostly M2 macrophages, is associated with poor prognosis of EOC patients. Further understanding of the interaction of M2 macrophages, MDR1 and TA-MUC1 appears to be a key aspect to overcome chemoresistance in ovarian cancer.

(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d’Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.