Emerging molecular alterations leading to histology-specific targeted therapies in ovarian cancer beyond PARP inhibitors

Targeted therapy in high grade serous ovarian Cancer: A literature review

Ovarian cancer continues to have a high mortality rate despite therapeutic advances. Traditionally, treatment has focused on surgery followed by systemic platinum- based chemotherapy. Unfortunately, most patients develop resistance to platinum agents, highlighting the need for targeted therapies. PARP inhibitors and anti-angiogenic agents, such as bevacizumab, have more recently changed upfront therapy. Unfortunately, other targeted therapies including immunotherapy have not seen the same success. Emerging therapeutic targets and modalities such as small molecule tyrosine kinase inhibitors, lipid metabolism targeting agents, gene therapy, ribosome targeted drugs as well as several other therapeutic classes have been and are currently under investigation. In this review, we discuss targeted therapies in high grade serous ovarian cancer from preclinical studies to phase III clinical trials.

HER2 puzzle pieces: Non-Coding RNAs as keys to mechanisms, chemoresistance, and clinical outcomes in Ovarian cancer

Ovarian cancer (OC) presents significant challenges, characterized by limited treatment options and therapy resistance often attributed to dysregulation of the HER2 signaling pathway. Non-coding RNAs (ncRNAs) have emerged as key players in regulating gene expression in OC. This comprehensive review underscores the pivotal role of ncRNAs in modulating HER2 signaling, with a specific focus on their mechanisms, impact on chemoresistance, and prognostic/diagnostic implications. MicroRNAs, long non-coding RNAs, and circular RNAs have been identified as essential regulators in the modulation of the HER2 pathway. By directly targeting key components of the HER2 axis, these ncRNAs influence its activation and downstream signaling cascades. Dysregulated ncRNAs have been closely associated with chemoresistance, leading to treatment failures and disease progression in OC. Furthermore, distinct expression profiles of ncRNAs hold promise as reliable prognostic and diagnostic markers, facilitating personalized treatment strategies and enhancing disease outcome assessments. A comprehensive understanding of how ncRNAs intricately modulate HER2 signaling is imperative for the development of targeted therapies and the improvement of patient outcomes. The integration of ncRNA profiles into clinical practice has the potential to enhance prognostic and diagnostic accuracy in the management of ovarian cancer. Further research efforts are essential to validate the clinical utility of ncRNAs and elucidate their precise roles in the regulation of HER2 signaling. In conclusion, ncRNAs play a crucial role in governing HER2 signaling in ovarian cancer, impacting chemoresistance and providing valuable prognostic and diagnostic insights. The exploration of ncRNA-mediated HER2 modulation offers promising avenues for the development of personalized treatment approaches, ultimately advancing patient care and outcomes in OC.

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.

A novel dopamine receptor D2 antagonist (ONC206) potentiates the effects of olaparib in endometrial cancer

Poly ADP-ribose polymerase (PARP) inhibitors are effective therapies for cancer patients with homologous recombination (HR) deficient tumors. The imipridone ONC206 is an orally bioavailable dopamine receptor D2 antagonist and mitochondrial protease ClpP agonist that has anti-tumorigenic effects in endometrial cancer via induction of apoptosis, activation of the integrated stress response and modulation of PI3K/AKT signaling. Both PARP inhibitors and imipridones are being evaluated in endometrial cancer clinical trials but have yet to be explored in combination. In this manuscript, we evaluated the effects of the PARP inhibitor olaparib in combination with ONC206 in human endometrioid endometrial cancer cell lines and in a genetically engineered mouse model of endometrial cancer. Our results showed that simultaneous exposure of endometrial cancer cells to olaparib and ONC206 resulted in synergistic anti-proliferative effects and increased cellular stress and apoptosis in both cell lines, compared to either drug alone. The combination treatment also decreased expression of the anti-apoptotic protein Bcl-2 and reduced phosphorylation of AKT and S6, with greater effects compared to either drug alone. In the transgenic model of endometrial cancer, the combination of olaparib and ONC206 resulted in a more significant reduction in tumor weight in obese and lean mice compared to ONC206 alone or olaparib alone, together with a considerably decreased Ki-67 and enhanced H2AX expression in obese and lean mice. These results suggest that this novel dual therapy may be worthy of further exploration in clinical trials.

Aptamers Versus Vascular Endothelial Growth Factor (VEGF): A New Battle against Ovarian Cancer

Cancer is one of the diseases that causes a high mortality as it involves unregulated and abnormal cell growth proliferation that can manifest in any body region. One of the typical ovarian cancer symptoms is damage to the female reproductive system. The death rate can be reduced through early detection of the ovarian cancer. Promising probes that can detect ovarian cancer are suitable aptamers. Aptamers, i.e., so-called chemical antibodies, have a strong affinity for the target biomarker and can typically be identified starting from a random library of oligonucleotides. Compared with other probes, ovarian cancer targeting using aptamers has demonstrated superior detection effectiveness. Various aptamers have been selected to detect the ovarian tumor biomarker, vascular endothelial growth factor (VEGF). The present review highlights the development of particular aptamers that target VEGF and detect ovarian cancer at its earliest stages. The therapeutic efficacy of aptamers in ovarian cancer treatment is also discussed.

Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data

The integration of transcriptome and proteome analysis can lead to the discovery of a myriad of biological insights into ovarian cancer. Proteome, clinical, and transcriptome data about ovarian cancer were downloaded from TCGA's database. A LASSO-Cox regression was used to uncover prognostic-related proteins and develop a new protein prognostic signature for patients with ovarian cancer to predict their prognosis. Patients were brought together in subgroups using a consensus clustering analysis of prognostic-related proteins. To further investigate the role of proteins and protein-coding genes in ovarian cancer, additional analyses were performed using multiple online databases (HPA, Sangerbox, TIMER, cBioPortal, TISCH, and CancerSEA). The final resulting prognosis factors consisted of seven protective factors (P38MAPK, RAB11, FOXO3A, AR, BETACATENIN, Sox2, and IGFRb) and two risk factors (AKT_pS473 and ERCC5), which can be used to construct a prognosis-related protein model. A significant difference in overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI) curves were found in the training, testing, and whole sets when analyzing the protein-based risk score (p < 0.05). We also illustrated a wide range of functions, immune checkpoints, and tumor-infiltrating immune cells in prognosis-related protein signatures. Additionally, the protein-coding genes were significantly correlated with each other. EMTAB8107 and GSE154600 single-cell data revealed that the genes were highly expressed. Furthermore, the genes were related to tumor functional states (angiogenesis, invasion, and quiescence). We reported and validated a survivability prediction model for ovarian cancer based on prognostic-related protein signatures. A strong correlation was found between the signatures, tumor-infiltrating immune cells, and immune checkpoints. The protein-coding genes were highly expressed in single-cell RNA and bulk RNA sequencing, correlating with both each other and tumor functional states.

A targetable MYBL2-ATAD2 axis governs cell proliferation in ovarian cancer

The chromatin-modifying enzyme ATAD2 confers oncogenic competence and proliferative advantage in malignances. We previously identified ATAD2 as a marker and driver of cell proliferation in ovarian cancer (OC); however, the mechanisms whereby ATAD2 is regulated and involved in cell proliferation are still unclear. Here, we disclose that ATAD2 displays a classical G2/M gene signature, functioning to facilitate mitotic progression. ATAD2 ablation caused mitotic arrest and decreased the ability of OC cells to pass through nocodazole-arrested mitosis. ChIP-seq data analyses demonstrated that DREAM and MYBL2-MuvB (MMB), two switchable MuvB-based complexes, bind the CHR elements in the ATAD2 promoter, representing a typical feature and principle mechanism of the periodic regulation of G2/M genes. As a downstream target of MYBL2, ATAD2 deletion significantly impaired MYBL2-driven cell proliferation. Intriguingly, ATAD2 silencing also fed back to destabilize the MYBL2 protein. The significant coexpression of MYBL2 and ATAD2 at both the bulk tissue and single-cell levels highlights the existence of the MYBL2-ATAD2 signaling in OC patients. This signaling is activated during tumorigenesis and correlated with TP53 mutation, and its hyperactivation was found especially in high-grade serous and drug-resistant OCs. Disrupting this signaling by CRISPR/Cas9-mediated ATAD2 ablation inhibited the in vivo growth of OC in a subcutaneous tumor xenograft mouse model, while pharmacologically targeting this signaling with an ATAD2 inhibitor demonstrated high therapeutic efficacy in both drug-sensitive and drug-resistant OC cells. Collectively, we identified a novel MYBL2-ATAD2 proliferative signaling axis and highlighted its potential application in developing new therapeutic strategies, especially for high-grade serous and drug-resistant OCs.

Unveiling the prominent roles of circular RNAs ubiquitin binding associated protein 2 in cancers

Circular RNAs (circRNAs), a novel type of covalently closed non-coding RNAs, are widely expressed in eukaryotes and viruses. Accumulating evidence has shown that circRNAs play key roles in the pathophysiological changes process of human diseases and can affect cancer development and progression through regulating target genes expression, linear RNA transcription and protein generation. Recent studies had found that circRNA-UBAP2 (ubiquitin binding associated protein 2) was aberrantly expressed in various human tumors and could affect tumor cells proliferation, migration, invasion, cell cycle, anti-apoptosis, radioresistance, chemoresistance and other malignant biological behavioral progress. Mechanistic studies further revealed that circUBAP2 could affect the occurrence and development of human tumors through multiple different molecular regulatory pathways in vivo and in vitro. In addition, the abnormal expression of circUBAP2 was significantly correlated with the clinicopathological characteristics of malignant tumors and had potential value as biomarkers for the diagnosis and prognosis evaluation of cancer patients, which deserved further study. This review had summarized and discussed the oncogenic roles and clinical performances of circUBAP2 in various human malignancies with a focus on biological functions and molecular mechanisms, which could help to elevate the understanding to the roles of circRNAs and continue subsequent studies on circUBAP2.

Targeting NaPi2b in ovarian cancer

Novel biomarkers are needed to direct new treatments for ovarian cancer, a disease for which the standard of care remains heavily focused on platinum-based chemotherapy. Despite the success of PARP inhibitors, treatment options are limited, particularly in the platinum-resistant setting. NaPi2b is a cell surface sodium-dependent phosphate transporter that regulates phosphate homeostasis under normal physiological conditions and is a lineage marker that is expressed in select cancers, including ovarian, lung, thyroid, and breast cancers, with limited expression in normal tissues. Based on its increased expression in ovarian tumors, NaPi2b is a promising candidate to be studied as a biomarker for treatment and patient selection in ovarian cancer. In preclinical studies, the use of antibodies against NaPi2b showed that this protein can be exploited for tumor mapping and therapeutic targeting. Emerging data from phase 1 and 2 clinical trials in ovarian cancer have suggested that NaPi2b can be successfully detected in patient biopsy samples using immunohistochemistry, and the NaPi2b-targeting antibody-drug conjugate under evaluation appeared to elicit therapeutic responses. The aim of this review is to examine literature supporting NaPi2b as a novel biomarker for potential treatment and patient selection in ovarian cancer and to discuss the critical next steps and future analyses necessary to drive the study of this biomarker and therapeutic targeting forward.

Combined Usage of MDK Inhibitor Augments Interferon-γ Anti-Tumor Activity in the SKOV3 Human Ovarian Cancer Cell Line

Ovarian cancer (OC) is a particularly lethal disease due to intratumoral heterogeneity, resistance to traditional chemotherapy, and poor response to targeted therapy and immunotherapy. Interferon-γ (IFN-γ) is an attractive therapeutic cytokine, with positive responses achieved in multiple OC clinical trials. However, clinical application of IFN-γ in OC is still hindered, due to the severe toxicity when used at higher levels, as well as the considerable pro-metastatic adverse effect when used at lower levels. Thus, an effective combined intervention is needed to enhance the anti-tumor efficacy of IFN-γ and to suppress the IFN-γ-induced metastasis. Here, we uncovered that OC cells develop an adaptive strategy by upregulating midkine (MDK) to counteract the IFN-γ-induced anti-tumor activity and to fuel IFN-γ-induced metastasis. We showed that MDK is a critical downstream target of IFN-γ in OC, and that this regulation acts in a dose-dependent manner and is mediated by STAT1. Gain-of-function studies showed that MDK overexpression promotes cell proliferation and metastasis in OC, indicating that IFN-γ-activated MDK may antagonize IFN-γ in inhibiting OC proliferation but synergize IFN-γ in promoting OC metastasis. Subsequently, we assessed the influence of MDK inhibition on IFN-γ-induced anti-proliferation and pro-metastasis effects using an MDK inhibitor (iMDK), and we found that MDK inhibition robustly enhanced IFN-γ-induced growth inhibition (all CIs < 0.1) and reversed IFN-γ-driven epithelial-to-mesenchymal transition (EMT) and metastasis in OC in vitro. Collectively, these data identify an IFN-γ responsive protein, MDK, in counteracting anti-proliferation while endowing the pro-metastatic role of IFN-γ in cancer treatment, and we therefore propose the combined utilization of the MDK inhibitor in IFN-γ-based therapies in future OC treatment.

Experimental drugs for fallopian cancer: promising agents in the clinical trials and key stumbling blocks for researchers

INTRODUCTION

Fallopian tube carcinoma (FC) as a single entity is a rare disease. Although its diagnosis is increasing thanks to the widespread use of prophylactic salpingectomy, there are no clinical trials exclusively designed for FC.

AREAS COVERED

This review aims at identifying the most promising trials and future therapeutic pathways in the setting of FC.

EXPERT OPINION

Hot topics in FC treatment include the consequences of using PARP inhibitors (PARPi) as first-line therapy, ways to overcome platinum resistance, and the role of immunotherapy. Patient selection is a key point for future development of target therapies. Next-generation sequencing (NGS) is one of the most investigated technologies both for drug discovery and identification of reverse mutations, involved in resistance to PARPi and platinum. New, promising molecular targets are emerging. Notwithstanding the disappointing outcomes when used by itself, immunotherapy in FC treatment could still have a role in combination with other agents, exploiting synergistic effects at the molecular level. The development of cancer vaccines is currently hampered by the high variability of tumor neoantigens in FC. Genomic profiling could be a solution, allowing the synthesis of individualized vaccines.

Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images

Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54-0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.

Molecular Biomarkers for the Early Detection of Ovarian Cancer

Ovarian cancer is the deadliest gynecological cancer, leading to over 152,000 deaths each year. A late diagnosis is the primary factor causing a poor prognosis of ovarian cancer and often occurs due to a lack of specific symptoms and effective biomarkers for an early detection. Currently, cancer antigen 125 (CA125) is the most widely used biomarker for ovarian cancer detection, but this approach is limited by a low specificity. In recent years, multimarker panels have been developed by combining molecular biomarkers such as human epididymis secretory protein 4 (HE4), ultrasound results, or menopausal status to improve the diagnostic efficacy. The risk of ovarian malignancy algorithm (ROMA), the risk of malignancy index (RMI), and OVA1 assays have also been clinically used with improved sensitivity and specificity. Ongoing investigations into novel biomarkers such as autoantibodies, ctDNAs, miRNAs, and DNA methylation signatures continue to aim to provide earlier detection methods for ovarian cancer. This paper reviews recent advancements in molecular biomarkers for the early detection of ovarian cancer.