Cisplatin Resistance in Ovarian Cancer: Classical Outlook and Newer Perspectives

Ovarian cancer is one of the most common gynecological cancers. Recently, there is increase in incidence of ovarian cancer not only India but also worldwide. Ovarian cancer patients exhibit nonspecific symptoms during early course of disease. As a consequence, 70% of these patients are diagnosed in advanced stages. Ovarian cancer treatment includes cytoreductive surgery followed by platinum-based chemotherapy. However, these patients develop fatal recurrence due to development of platinum resistance. Cisplatin, (platinum analog) resistance is multifactorial and complex. Earlier, resistance was mainly attributed to conventional molecular mechanisms like decreased intracellular accumulation of cisplatin, enhanced DNA repair and increased cisplatin detoxification. Nevertheless, emergence of knowledge of tumor biology have lead to discovery of other contributing mechanisms. These tumor microenvironment related factors include physical blockade, hypoxia, cancer stem cells, cancer associated fibroblasts and many others. Understanding these mechanisms of cisplatin resistance is crucial for development of novel strategy to combat the same. Hence, this review summarizes all the mechanisms of resistance of cisplatin in ovarian cancer.

ZNF76 predicts prognosis and response to platinum chemotherapy in human ovarian cancer

Ovarian cancer (OV) is the most lethal gynecologic malignancy. One major reason of the high mortality of the disease is due to platinum-based chemotherapy resistance. Increasing evidence reveals the important biological functions and clinical significance of zinc finger proteins (ZNFs) in OV. In this study, the relationship between zinc finger protein 76 (ZNF76) and clinical outcome and platinum resistance in patients with OV was explored. We further analyzed ZNF76 expression via multiple gene expression databases and identified its functional networks using cBioPortal. RT-qPCR and IHC assay shown that the ZNF76 mRNA and protein expression were significantly lower in OV tumor than that in normal ovary tissues. A strong relationship between ZNF76 expression and platinum resistance was determined in patients with OV. The low expression of ZNF76 was associated with worse survival in OV. Multivariable analysis showed that the low expression of ZNF76 was an independent factor predicting poor outcome in OV. The prognosis value of ZNF76 in pan-cancer was validated from multiple cohorts using the PrognoScan database and GEPIA 2. A gene-clinical nomogram was constructed by multivariate cox regression analysis, combined with clinical characterization and ZNF76 expression in TCGA. Functional network analysis suggested that ZNF76 was involved in several biology progressions which associated with OV. Nine hub genes (CDC5L, DHX16, SNRPC, LSM2, CUL7, PFDN6, VARS, HSD17B8, and RGL2) were identified as positively associated with the expression of ZNF76 in OV. In conclusion, ZNF76 may serve as a promising prognostic-related biomarker and predict the response to platinum in OV patients.

SCD1/FADS2 fatty acid desaturases equipoise lipid metabolic activity and redox-driven ferroptosis in ascites-derived ovarian cancer cells

Malignant ascites in peritoneal metastases is a lipid-enriched microenvironment and is frequently involved in the poor prognosis of epithelial ovarian cancer (EOC). However, the detailed mechanisms underlying ovarian cancer (OvCa) cells dictating their lipid metabolic activities in promoting tumor progression remain elusive. Here, we report that two critical fatty acid desaturases, stearoyl-CoA desaturase-1 (SCD1) and acyl-CoA 6-desaturase (FADS2), are aberrantly upregulated, accelerating lipid metabolic activities and tumor aggressiveness of ascites-derived OvCa cells. Lipidomic analysis revealed that the elevation of unsaturated fatty acids (UFAs) is positively associated with SCD1/FADS2 levels and the oncogenic capacities of OvCa cells. In contrast, pharmaceutical inhibition and genetic ablation of SCD1/FADS2 retarded tumor growth, suppressed cancer stem cell (CSC) formation and reduced platinum resistance in OvCa cells. Mechanistically, inhibition of SCD1/FADS2 directly downregulated GPX4 and the GSH/GSSG ratio, causing disruption of the cellular redox balance and subsequent iron-mediated lipid peroxidation in ascites-derived OvCa cells. Hence, combinational treatment with SCD1/FADS2 inhibitors and cisplatin synergistically repressed tumor cell dissemination, providing a promising chemotherapeutic strategy against EOC platinum resistance and peritoneal metastases.

Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge

Evidences increasingly indicate the involvement of gene network rewiring in disease development and cell differentiation. With the accumulation of high-throughput gene expression data, it is now possible to infer the changes of gene networks between two different states or cell types via computational approaches. However, the distribution diversity of multi-platform gene expression data and the sparseness and high noise rate of single-cell RNA sequencing (scRNA-seq) data raise new challenges for existing differential network estimation methods. Furthermore, most existing methods are purely rely on gene expression data, and ignore the additional information provided by various existing biological knowledge. In this study, to address these challenges, we propose a general framework, named weighted joint sparse penalized D-trace model (WJSDM), to infer differential gene networks by integrating multi-platform gene expression data and multiple prior biological knowledge. Firstly, a non-paranormal graphical model is employed to tackle gene expression data with missing values. Then we propose a weighted group bridge penalty to integrate multi-platform gene expression data and various existing biological knowledge. Experiment results on synthetic data demonstrate the effectiveness of our method in inferring differential networks. We apply our method to the gene expression data of ovarian cancer and the scRNA-seq data of circulating tumor cells of prostate cancer, and infer the differential network associated with platinum resistance of ovarian cancer and anti-androgen resistance of prostate cancer. By analyzing the estimated differential networks, we find some important biological insights about the mechanisms underlying platinum resistance of ovarian cancer and anti-androgen resistance of prostate cancer.

Identification of keygenes, miRNAs and miRNA-mRNA regulatory pathways for chemotherapy resistance in ovarian cancer

Background Chemotherapy resistance, especially platinum resistance, is the main cause of poor prognosis of ovarian cancer. It is of great urgency to find molecular markers and mechanism related to platinum resistance in ovarian cancer. Methods One mRNA dataset (GSE28739) and one miRNA dataset (GSE25202) were acquired from Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) between platinum-resistant and platinum-sensitive ovarian cancer patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed using the DAVID to present the most visibly enriched pathways. Protein–protein interaction (PPI) of these DEGs was constructed based on the information of the STRING database. Hub genes related to platinum resistance were visualized by Cytoscape software. Then, we chose seven interested hub genes to further validate using qRT-PCR in A2780 ovarian cancer cell lines. And, at last, the TF-miRNA-target genes regulatory network was predicted and constructed using miRNet software. Results A total of 63 upregulated DEGs, 124 downregulated DEGs, four upregulated miRNAs and six downregulated miRNAs were identified. From the PPI network, the top 10 hub genes were identified, which were associated with platinum resistance. Our further qRT-PCR showed that seven hub genes (BUB1, KIF2C, NUP43, NDC80, NUF2, CCNB2 and CENPN) were differentially expressed in platinum-resistant ovarian cancer cells. Furthermore, the upstream transcription factors (TF) for upregulated DE-miRNAs were SMAD4, NFKB1, SMAD3, TP53 and HNF4A. Three overlapping downstream target genes (KIF2C, STAT3 and BUB1) were identified by miRNet, which was regulated by hsa-miR-494. Conclusions The TF-miRNA–mRNA regulatory pairs, that is TF (SMAD4, NFKB1 and SMAD3)-miR-494-target genes (KIF2C, STAT3 and BUB1), were established. In conclusion, the present study is of great significance to find the key genes of platinum resistance in ovarian cancer. Further study is needed to identify the mechanism of these genes in ovarian cancer.

Neutrophil-to-lymphocyte ratio and chemotherapy response score as prognostic markers in ovarian cancer patients treated with neoadjuvant chemotherapy

Background Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is the recommended approach in patients with advanced epithelial ovarian cancer (EOC). However, most patients eventually relapse despite the initial high response rate to chemotherapy. Neutrophil-to-lymphocyte ratio is a well-known biomarker that reflects severe inflammation, critical illness, and mortality in various diseases. Chemotherapy response score (CRS) and neutrophil-to-lymphocyte ratio (NLR) have been identified as potential biomarkers of platinum resistance and disease prognosis. We retrospectively evaluated 132 patients with stage IIIc or IV ovarian/fallopian tube/primary peritoneal cancer who had received NACT followed by IDS from 01/01/2003 to 31/12/2018. CRS was assessed on omental specimens collected from IDS according to ICCR guidelines. Results Median age was 64.57 years (SD: 9.72; range 39.2–87.1). Most ovarian tumors were serous epithelial (90.9%; 120/132). An elevated NLR (defined as > 3) was observed in 72% (95/132) of patients in contrast with 28% (37/132) of patients characterized by low NLR status. Median PFS (mPFS) and median overall survival (mOS) were 13.05 months (95% CI: 11.42–14.67)) and 34.69 months (95% CI: 23.26–46.12) respectively. In univariate analysis, CRS3 score was significantly associated with prolonged mPFS (CRS1/2: 12.79 months vs CRS3: 17.7 months; P = 0.008). CRS score was not associated with mOS (P = 0.876). High NLR was not significantly associated with mPFS (P = 0.128), however it was significantly associated with poor mOS (P = 0.012). In multivariate analysis, only performance of surgery maintained its statistical significance with both PFS (P = 0.001) and OS (P = 0.008). Conclusion NLR could serve as a useful predictor of OS but not PFS in ovarian cancer patients receiving NACT. In accordance with our previous study, CRS score at omentum was found to be associated with PFS but not OS in ovarian cancer patients treated with NACT and IDS.