Synthetic lethality guiding selection of drug combinations in ovarian cancer

Extracellular vesicles as a potential delivery platform for CRISPR-Cas based therapy in epithelial ovarian cancer

Ovarian Cancer (OC) is the most common gynecological malignancy and the eighth most diagnosed cancer in females worldwide. Presently, it ranks as the fifth leading cause of cancer-related mortality among patients globally. Major factors contributing to the lethality of OC worldwide include delayed diagnosis, chemotherapy resistance, high metastatic rates, and the heterogeneity of subtypes. Despite continuous efforts to develop novel targeted therapies and chemotherapeutic agents, challenges persist in the form of OC resistance and recurrence. In the last decade, CRISPR-Cas-based genome editing has emerged as a powerful tool for modifying genetic and epigenetic mechanisms, holding potential for treating numerous diseases. However, a significant challenge for therapeutic applications of CRISPR-Cas technology is the absence of an optimal vehicle for delivering CRISPR molecular machinery into targeted cells or tissues. Recently, extracellular vesicles (EVs) have gained traction as potential delivery vehicles for various therapeutic agents. These heterogeneous, membrane-derived vesicles are released by nearly all cells into extracellular spaces. They carry a molecular cargo of proteins and nucleic acids within their intraluminal space, encased by a cholesterol-rich phospholipid bilayer membrane. EVs actively engage in cell-to-cell communication by delivering cargo to both neighboring and distant cells. Their inherent ability to shield molecular cargo from degradation and cross biological barriers positions them ideally for delivering CRISPR-Cas ribonucleoproteins (RNP) to target cells. Furthermore, they exhibit higher biocompatibility, lower immunogenicity, and reduced toxicity compared to classical delivery platforms such as adeno-associated virus, lentiviruses, and synthetic nanoparticles. This review explores the potential of employing different CRISPR-Cas systems to target specific genes in OC, while also discussing various methods for engineering EVs to load CRISPR components and enhance their targeting capabilities.

PRMT5 promotes ovarian cancer growth through enhancing Warburg effect by methylating ENO1

Protein arginine methyltransferase 5 (PRMT5) is a major type II enzyme responsible for symmetric dimethylation of arginine (SDMA), and plays predominantly roles in human cancers, including in ovarian cancer. However, the exactly roles and underlying mechanisms of PRMT5 contributing to the progression of ovarian cancer mediated by reprogramming cell metabolism remain largely elusive. Here, we report that PRMT5 is highly expressed and correlates with poor survival in ovarian cancer. Knockdown or pharmaceutical inhibition of PRMT5 is sufficient to decrease glycolysis flux, attenuate tumor growth, and enhance the antitumor effect of Taxol. Mechanistically, we find that PRMT5 symmetrically dimethylates alpha-enolase (ENO1) at arginine 9 to promotes active ENO1 dimer formation, which increases glycolysis flux and accelerates tumor growth. Moreover, PRMT5 signals high glucose to increase the methylation modification of ENO1. Together, our data reveal a novel role of PRMT5 in promoting ovarian cancer growth by controlling glycolysis flux mediated by methylating ENO1, and highlights that PRMT5 may represent a promising therapeutic target for treating ovarian cancer.

Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer

Breast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on the estrogen receptor α (ERα) for tumor progression. Therefore, targeting ERα with antagonists, such as tamoxifen, or estrogen deprivation by aromatase inhibitors remain standard therapies for ERα + breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, decrease toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERα + breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERα + /HER2-/PI3Kα-mutant subtype of breast cancer. The 3-drug combination targets ERα in combination with PI3Kα and cyclin-dependent kinase inhibitor 1 (p21). In addition, the 4-drug combination contains an inhibitor for poly (ADP-ribose) polymerase 1 (PARP1), which showed benefits in long-term treatments. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.

SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL:  https://synlethdb.sist.shanghaitech.edu.cn/v2.

Synthetic Lethality in Ovarian Cancer

Ovarian cancers include several distinct malignancies which differ with respect to clinicopathologic features and prognosis. High-grade serous cancer is the most common histologic subtype and accounts for most ovarian cancer-related deaths. High-grade serous ovarian cancer (HGSOC) is treated with surgery and platinum-based chemotherapy, but most patients relapse and succumb to chemoresistant disease. The genetic concept of synthetic lethality, in which the synergy of mutations in multiple genes results in cell death, provides a framework to design novel therapeutic approaches to overcome chemoresistance in ovarian cancer. Recent progress in understanding the genomic architecture and hereditary drivers of ovarian cancer has shown potential for synthetic lethality strategies designed around homologous DNA repair. Clinical trials have validated high response rates for PARP inhibitors in patients with BRCA1 or BRCA2 mutations. Here we discuss the biological rationale behind targeting BRCA-PARP synthetic lethality based on genetic context in ovarian cancer and how this approach is being assessed in the clinic. Applying the concept of synthetic lethality to target non-BRCA-mutant cancers is an ongoing challenge, and we discuss novel approaches to target ovarian cancer using synthetic lethality in combination with and beyond PARP inhibitors. This review will also describe obstacles for synthetic lethality in ovarian cancer and new opportunities to develop potent targeted drugs for patients with ovarian cancer.

Poly(ADP-ribose) polymerase inhibitors in combination with anti-angiogenic agents for the treatment of advanced ovarian cancer

Homologous recombination deficiency and VEGF expression are key pathways in high-grade ovarian cancer. Recently, three randomized practice changing trials were published: the PAOLA-1, PRIMA and VELIA trials. The use of PARP inhibitors (PARPi) following chemotherapy has become standard of care in first line. Combination of PARPi with anti-angiogenic agents has demonstrated synergistic activity in preclinical study. This review summarizes the body of evidence supporting the efficacy and safety of the combination of PARPi and anti-angiogenic drugs in first-line homologous recombination deficiency high-grade ovarian cancer leading to US FDA and EMA approvals. This double maintenance is supported by: a large benefit with bevacizumab + olaparib compared with olaparib alone, a rationale for additive effect, and a good safety and cost-effective profile.

Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers

MOTIVATION

Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture such neighborhood dependency in a graph. However, it is still challenging to apply GCN for SL prediction as SL interactions are extremely sparse, which is more likely to cause overfitting.

RESULTS

In this article, we propose a novel dual-dropout GCN (DDGCN) for learning more robust gene representations for SL prediction. We employ both coarse-grained node dropout and fine-grained edge dropout to address the issue that standard dropout in vanilla GCN is often inadequate in reducing overfitting on sparse graphs. In particular, coarse-grained node dropout can efficiently and systematically enforce dropout at the node (gene) level, while fine-grained edge dropout can further fine-tune the dropout at the interaction (edge) level. We further present a theoretical framework to justify our model architecture. Finally, we conduct extensive experiments on human SL datasets and the results demonstrate the superior performance of our model in comparison with state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

DDGCN is implemented in Python 3.7, open-source and freely available at https://github.com/CXX1113/Dual-DropoutGCN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Immunotherapy Advances for Epithelial Ovarian Cancer

Simple Summary

The overall five-year survival rate in epithelial ovarian cancer is 44% and has only marginally improved in the past two decades. Despite an initial response to standard treatment consisting of chemotherapy and surgical removal of tumor, the lesions invariably recur, and patients ultimately die of chemotherapy resistant disease. New treatment modalities are needed in order to improve the prognosis of women diagnosed with ovarian cancer. One such modality is immunotherapy, which aims to boost the capacity of the patient’s immune system to recognize and attack the tumor cells. We performed a retrospective study to identify some of the most promising immune therapies for epithelial ovarian cancer. Special emphasis was given to immuno-oncology clinical trials.

Abstract

New treatment modalities are needed in order to improve the prognosis of women diagnosed with epithelial ovarian cancer (EOC), the most aggressive gynecologic cancer type. Most ovarian tumors are infiltrated by immune effector cells, providing the rationale for targeted approaches that boost the existing or trigger new anti-tumor immune mechanisms. The field of immuno-oncology has experienced remarkable progress in recent years, although the results seen with single agent immunotherapies in several categories of solid tumors have yet to extend to ovarian cancer. The challenge remains to determine what treatment combinations are most suitable for this disease and which patients are likely to benefit and to identify how immunotherapy should be incorporated into EOC standard of care. We review here some of the most promising immune therapies for EOC and focus on those currently tested in clinical trials.

Synthetic Lethality through the Lens of Medicinal Chemistry

Personalized medicine and therapies represent the goal of modern medicine, as drug discovery strives to move away from one-cure-for-all and makes use of the various targets and biomarkers within differing disease areas. This approach, especially in oncology, is often undermined when the cells make use of alternative survival pathways. As such, acquired resistance is unfortunately common. In order to combat this phenomenon, synthetic lethality is being investigated, making use of existing genetic fragilities within the cancer cell. This Perspective highlights exciting targets within synthetic lethality, (PARP, ATR, ATM, DNA-PKcs, WEE1, CDK12, RAD51, RAD52, and PD-1) and discusses the medicinal chemistry programs being used to interrogate them, the challenges these programs face, and what the future holds for this promising field.

Are antiangiogenics a good 'partner' for immunotherapy in ovarian cancer?

Ovarian cancer (OC) is associated with poor survival because there are a limited number of effective therapies. Two processes key to OC progression, angiogenesis and immune evasion, act synergistically to promote tumor progression. Tumor-associated angiogenesis promotes immune evasion, and tumor-related immune responses in the peritoneal cavity and tumor microenvironment (TME) affect neovascular formation. Therefore, suppressing the angiogenic pathways could facilitate the arrival of immune effector cells and reduce the presence of myeloid cells involved in immune suppression. To date, clinical studies have shown significant benefits with antiangiogenic therapy as first-line therapy in OC, as well as in recurrent disease, and the vascular endothelial growth factor (VEGF) inhibitor bevacizumab is now an established therapy. Clinical data with immunomodulators in OC are more limited, but suggest that they could benefit some patients with recurrent disease. The preliminary results of two phase III trials have shown that the addition of immunomodulators to chemotherapy does not improve progression-free survival. For this reason, it could be interesting to look for synergistic effects between immunomodulators and other active drugs in OC. Since bevacizumab is approved for use in OC, and is tolerable when used in combination with immunotherapy in other indications, a number of clinical studies are underway to investigate the use of bevacizumab in combination with immunotherapeutic agents in OC. This strategy seeks to normalize the TME via the anti-VEGF actions of bevacizumab, while simultaneously stimulating the immune response via the immunotherapy. Results of these studies are awaited with interest.

RNA-binding protein DDX3 mediates posttranscriptional regulation of androgen receptor: A mechanism of castration resistance

Prostate cancer (CaP) driven by androgen receptor (AR) is treated with androgen deprivation; however, therapy failure results in lethal castration-resistant prostate cancer (CRPC). AR-low/negative (ARL/-) CRPC subtypes have recently been characterized and cannot be targeted by hormonal therapies, resulting in poor prognosis. RNA-binding protein (RBP)/helicase DDX3 (DEAD-box helicase 3 X-linked) is a key component of stress granules (SG) and is postulated to affect protein translation. Here, we investigated DDX3-mediated posttranscriptional regulation of AR mRNA (messenger RNA) in CRPC. Using patient samples and preclinical models, we objectively quantified DDX3 and AR expression in ARL/- CRPC. We utilized CRPC models to identify DDX3:AR mRNA complexes by RNA immunoprecipitation, assess the effects of DDX3 gain/loss-of-function on AR expression and signaling, and address clinical implications of targeting DDX3 by assessing sensitivity to AR-signaling inhibitors (ARSI) in CRPC xenografts in vivo. ARL/- CRPC expressed abundant AR mRNA despite diminished levels of AR protein. DDX3 protein was highly expressed in ARL/- CRPC, where it bound to AR mRNA. Consistent with a repressive regulatory role, DDX3 localized to cytoplasmic puncta with SG marker PABP1 in CRPC. While induction of DDX3-nucleated SGs resulted in decreased AR protein expression, inhibiting DDX3 was sufficient to restore 1) AR protein expression, 2) AR signaling, and 3) sensitivity to ARSI in vitro and in vivo. Our findings implicate the RBP protein DDX3 as a mechanism of posttranscriptional regulation for AR in CRPC. Clinically, DDX3 may be targetable for sensitizing ARL/- CRPC to AR-directed therapies.

Synthetic lethality on drug discovery: an update on cancer therapy

INTRODUCTION

A novel anticancer therapy is the need of the hour due to growing incidences of resistance to first line cancer chemotherapy. Synthetic lethality (SL) is one of the new age treatment methods being explored for combating the resistance to anticancer agents. In this method, cell mutations are exploited for the development of new therapeutic agents, where, if there is loss of function of one gene, the cell mutations can still be fixed by alternative machinery but if two genes involved in DNA repair undergo loss of function, it causes lethality to the cell.

AREAS COVERED

The authors condense findings of SL-based novel anticancer regimen. The review emphasizes some of the SL based clinical and preclinical studies of novel targets and therapy.

EXPERT OPINION

SL conceptualizes a resolution against treatment resistance to anticancer regimen by recognition of therapeutic vulnerabilities in particular cancer cells. A multitude of clinical trials associated with SL and DNA repair are being conducted that will be useful in obtaining a clearer picture pertaining to the use of cancer biomarkers and effectiveness of drugs acting via target-based molecular changes. Furthermore, new anticancer regimen focused on personalized medicines will emerge basing their development upon SL.

Targeted activation of Stat3 in combination with paclitaxel results in increased apoptosis in epithelial ovarian cancer cells and a reduced tumour burden

Objectives

Stat3 is persistently activated in ovarian cancer cells, with a crucial role in tumour onset and progression. In this study, we examined the anti‐tumour effect of a small‐molecule inhibitor napabucasin (BBI608) on epithelial ovarian cancer (EOC) in vitro and in vivo, and investigated the underlying molecular mechanism of this drug in combination with paclitaxel.

Materials and Methods

A total of 156 ovarian cancer patient samples were analysed to determine the correlation between pStat3 expression in tumour cells and the prognosis of EOC patients. The anti‐tumour effect of BBI608 and/or paclitaxel on ovarian cancer in vitro was evaluated by CCK‐8, flow cytometry, Western blot and transwell assays. An in vivo intraperitoneal model was performed to confirm the effect of BBI608 on pStat3‐mediated peritoneal metastasis when combined with paclitaxel.

Results

Patients with high expression of pStat3 had poorer overall survival and progression‐free survival than those with low pStat3 expression. The synergy of BBI608 in combination with paclitaxel exerted dramatic growth inhibition and induced apoptosis in EOC cell lines. In vivo, the combination of two drugs significantly decreased intraperitoneal tumour burden and ascites volume, prolonged survival of tumour‐bearing mice compared with each monotherapy; these results were associated with downregulation of phospho‐Stat3 and activation of apoptosis pathway.

Conclusions

Targeting the activation of Stat3 may be a potential therapeutic approach for EOC by acting synergistically with paclitaxel.

Highlights in Resistance Mechanism Pathways for Combination Therapy

Combination chemotherapy has been a mainstay in cancer treatment for the last 60 years. Although the mechanisms of action and signaling pathways affected by most treatments with single antineoplastic agents might be relatively well understood, most combinations remain poorly understood. This review presents the most common alterations of signaling pathways in response to cytotoxic and targeted anticancer drug treatments, with a discussion of how the knowledge of signaling pathways might support and orient the development of innovative strategies for anticancer combination therapy. The ultimate goal is to highlight possible strategies of chemotherapy combinations based on the signaling pathways associated with the resistance mechanisms against anticancer drugs to maximize the selective induction of cancer cell death. We consider this review an extensive compilation of updated known information on chemotherapy resistance mechanisms to promote new combination therapies to be to discussed and tested.