Molecular Management of High-Grade Serous Ovarian Carcinoma

Targeting estrogen metabolism in high-grade serous ovarian cancer shows promise to overcome platinum resistance

The high mortality rate due to chemoresistance in patients with high-grade ovarian cancer (HGSOC) emphasizes the urgent need to determine optimal treatment strategies for advanced and recurrent cases. Our study investigates the interplay between estrogens and chemoresistance in HGSOC and shows clear differences between platinum-sensitive and -resistant tumors. Through comprehensive transcriptome analyzes, we uncover differences in the expression of genes of estrogen biosynthesis, metabolism, transport and action underlying platinum resistance in different tissues of HGSOC subtypes and in six HGSOC cell lines. Furthermore, we identify genes involved in estrogen biosynthesis and metabolism as prognostic biomarkers for HGSOC. Additionally, our study elucidates different patterns of estrogen formation/metabolism and their effects on cell proliferation between six HGSOC cell lines with different platinum sensitivity. These results emphasize the dynamic interplay between estrogens and HGSOC chemoresistance. In particular, targeting the activity of steroid sulfatase (STS) proves to be a promising therapeutic approach with potential efficacy in limiting estrogen-driven cell proliferation. Our study reveals potential prognostic markers as well as identifies novel therapeutic targets that show promise for overcoming resistance and improving treatment outcomes in HGSOC.

Fluorescent imaging probes for in vivo ovarian cancer targeted detection and surgery

Ovarian cancer is the most lethal gynecological cancer, with a survival rate of approximately 40% at five years from the diagno. The first-line treatment consists of cytoreductive surgery combined with chemotherapy (platinum- and taxane-based drugs). To date, the main prognostic factor is related to the complete surgical resection of tumor lesions, including occult micrometastases. The presence of minimal residual diseases not detected by visual inspection and palpation during surgery significantly increases the risk of disease relapse. Intraoperative fluorescence imaging systems have the potential to improve surgical outcomes. Fluorescent tracers administered to the patient may support surgeons for better real-time visualization of tumor lesions during cytoreductive procedures. In the last decade, consistent with the discovery of an increasing number of ovarian cancer-specific targets, a wide range of fluorescent agents were identified to be employed for intraoperatively detecting ovarian cancer. Here, we present a collection of fluorescent probes designed and developed for fluorescence-guided ovarian cancer surgery. Original articles published between 2011 and November 2022 focusing on fluorescent probes, currently under preclinical and clinical investigation, were searched in PubMed. The keywords used were targeted detection, ovarian cancer, fluorescent probe, near-infrared fluorescence, fluorescence-guided surgery, and intraoperative imaging. All identified papers were English-language full-text papers, and probes were classified based on the location of the biological target: intracellular, membrane, and extracellular.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine

Background

Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m6A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m6A modification-associated lncRNAs.

Methods

Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m6A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs.

Results

Six m6A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m6A effector-related lncRNA risk model (m6A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m6A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m6A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m6A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses.

Conclusion

In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m6A-related lncRNAs in SOC. Incorporating the m6A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

Identification of estrogen response-associated STRA6+ granulosa cells within high-grade serous ovarian carcinoma by single-cell sequencing

Background

High-grade serous ovarian carcinoma (HGSOC) is a pathologic subtype of ovarian cancer (OC) with a more lethal prognosis. Extensive heterogeneity results in HGSOC being more susceptible to treatment resistance and adverse treatment effects. Revealing the heterogeneity involved is crucial.

Methods

We downloaded the single-cell RNA-seq (scRNA) data from GEO database and performed a scRNA analysis for cell landscape of HGSOC by using the Seurat package. The highly expressed genes were uploaded into the DAVID and KEGG database for enrichment analysis, and the AUCell package was used to calculate cancer-associated hallmark score. The SCENIC analysis was used for key regulons, the estrogen response enrichment scores in TCGA-OV RNA-seq dataset were calculated by using the GSVA package. Besides, the expression of STRA6 and IRF1 and the cell invasion and migration in si-STRA6 OC cells were detected by using the quantitative reverse transcription (qRT)-PCR method and Transwell assay respectively.

Results

We successfully constructed a single-cell atlas of HGSOC and delineated the heterogeneity of epithelial cells therein. There were five epithelial cell subpopulations, GLDC + Epithelial cells, PEG3+ leydig cells, STRA6+ granulosa cells, POLE2+ Epithelial cells, and AURKA + Epithelial cells. STRA6+ granulosa cells have the potential to promote tumor growth as well as the highest estrogen response early activity through the biological pathways analysis of highly expressed genes and estrogen response score of ssGSEA. We found that IRF1 and STRA6 expression was remarkably upregulated in the OC cancer cell line HEY. Silencing of STRA6 markedly decreased the invasion and migration ability of the OC cancer cell line HEY.

Conclusion

There is extreme heterogeneity of epithelial cells in HGSOC, and STRA6+ granulosa cells may be able to promote cancer progression. Our findings are benefit to the heterogeneity identification of HGSOC and develop targeted therapy strategy for HGSOC patients.

High-grade serous ovarian carcinoma, the "Achiles' hill" for clinicians and molecular biologists: a molecular insight

High-grade serous ovarian carcinoma (HGSOC), the deadliest ovarian cancer, alone accounts for 90% of all its subtypes. Characterized by hallmark mutation of TP53, HGSOC show diverse molecular etiology. HGSOC can arise from both ovarian epithelium as well as the fimbrial epithelium of the fallopian tube. Ovulation induced reactive oxygen species, follicular fluid associated growth factor induced stemness, deregulation of hormone receptors like ER, FSHR, AR and hormones like FSH, LH, prolonged ovulation cycle, use of oral contraceptives are agonists of HGSOC while parity, breastfeeding provide protective effect from HGSOC development. Apart from a generic TP53 mutation, mutation of BRCA1/2, RAD51, BRIP1, PALB2, CHEK2, RAD50 etc., were reportedly associated with development of HGSOC. Epigenetic events like methylation of RASSF1A of RAS signaling pathway,OR51L1, OR51I1, OR51F1 etc. has been reported in HGSOC. Micro-RNAs like miR-1290, miR 27-a-3p miR23a, miR205 were reportedly upregulated in HGSOC. Amongst its cognate subtypes viz. differentiated, immunoreactive, mesenchymal, and proliferative, mesenchymal, and proliferative show worst prognosis. A system biology approach showed five major altered pathways in HGSOC, namely, RB, PI3K/RAS, NOTCH, HRR and FOXM1 signaling. For chemonaive patients, drugs that helps in efflux of reduced glutathione or prevent the redox coupling of GSH-GSSG, like Cisplatin, could be considered as the best therapeutic choice for HGSOC. For patients with BRCA1/2 mutations, PARP inhibitors alone or with Bevacizumab can be effective. Immune checkpoint inhibitors could be effective against immunoreactive subtypes. Identification of genes deregulated in chemoresistance could provide better insights in dealing with the disease.

Integrative evaluation and experimental validation of the immune-modulating potential of dysregulated extracellular matrix genes in high-grade serous ovarian cancer prognosis

BACKGROUND

High-grade serous ovarian cancer (HGSOC) is a challenging malignancy characterized by complex interactions between tumor cells and the surrounding microenvironment. Understanding the immune landscape of HGSOC, particularly the role of the extracellular matrix (ECM), is crucial for improving prognosis and guiding therapeutic interventions.

METHODS AND RESULTS

Using univariate Cox regression analysis, we identified 71 ECM genes associated with prognosis in seven HGSOC populations. The ECMscore signature, consisting of 14 genes, was validated using Cox proportional hazards regression with a lasso penalty. Cox regression analyses demonstrated that ECMscore is an excellent indicator for prognostic classification in prevalent malignancies, including HGSOC. Moreover, patients with higher ECMscores exhibited more active stromal and carcinogenic activation pathways, including apical surface signaling, Notch signaling, apical junctions, Wnt signaling, epithelial-mesenchymal transition, TGF-beta signaling, and angiogenesis. In contrast, patients with relatively low ECMscores showed more active immune-related pathways, such as interferon alpha response, interferon-gamma response, and inflammatory response. The relationship between the ECMscore and genomic anomalies was further examined. Additionally, the correlation between ECMscore and immune microenvironment components and signals in HGSOC was examined in greater detail. Moreover, the expression of MGP, COL8A2, and PAPPA and its correlation with FAP were validated using qRT-PCR on samples from HGSOC. The utility of ECMscore in predicting the prospective clinical success of immunotherapy and its potential in guiding the selection of chemotherapeutic agents were also explored. Similar results were obtained from pan-cancer research.

CONCLUSION

The comprehensive evaluation of the ECM may help identify immune activation and assist patients in HGSOC and even pan-cancer in receiving proper therapy.

Diagnostics and treatment of ovarian cancer in the era of precision medicine - opportunities and challenges

Due to predictions of increasing incidences and deaths from ovarian cancer, this neoplasm is a challenge for modern health care. The advent of NGS technology has made it possible to understand the molecular characteristics of many cancers, including ovarian cancer. The data obtained in research became the basis for the development of molecularly targeted therapies thus leading to the entry of NGS analysis into the diagnostic process of oncological patients. This review presents targeted therapies currently in preclinical or clinical trials, whose promising results offer hope for their use in clinical practice in the future. As more therapeutic options emerge, it will be necessary to modify molecular diagnostic regimens to select the best treatment for a given patient. New biomarkers are needed to predict the success of planned therapy. An important aspect of public health is molecular testing in women with a familial predisposition to ovarian cancer enabling patients to be included in prevention programs. NGS technology, despite its high throughput, poses many challenges, from the quality of the diagnostic material used for testing to the interpretation of results and classification of sequence variants. The article highlights the role of molecular testing in ongoing research and also its role in the diagnostic and therapeutic process in the era of personalized medicine. The spread of genetic testing in high-risk groups, the introduction of more targeted therapies and also the possibility of agnostic therapies could significantly improve the health situation for many women worldwide.