The Role of Cancer-Associated Fibroblasts in Ovarian Cancer

Immunotherapy and the ovarian cancer microenvironment: Exploring potential strategies for enhanced treatment efficacy

Despite progress in cancer immunotherapy, ovarian cancer (OC) prognosis continues to be disappointing. Recent studies have shed light on how not just tumour cells, but also the complex tumour microenvironment, contribute to this unfavourable outcome of OC immunotherapy. The complexities of the immune microenvironment categorize OC as a 'cold tumour'. Nonetheless, understanding the precise mechanisms through which the microenvironment influences the effectiveness of OC immunotherapy remains an ongoing scientific endeavour. This review primarily aims to dissect the inherent characteristics and behaviours of diverse cells within the immune microenvironment, along with an exploration into its reprogramming and metabolic changes. It is expected that these insights will elucidate the operational dynamics of the immune microenvironment in OC and lay a theoretical groundwork for improving the efficacy of immunotherapy in OC management.

Extracellular Vesicles in Ovarian Cancer: From Chemoresistance Mediators to Therapeutic Vectors

Ovarian cancer (OC) remains the deadliest gynecological malignancy, with alarming projections indicating a 42% increase in new cases and a 51% rise in mortality by 2040. This review explores the challenges in OC treatment, focusing on chemoresistance mechanisms and the potential of extracellular vesicles (EVs) as drug delivery agents. Despite advancements in treatment strategies, including cytoreductive surgery, platinum-based chemotherapy, and targeted therapies, the high recurrence rate underscores the need for innovative approaches. Key resistance mechanisms include drug efflux, apoptosis disruption, enhanced DNA repair, cancer stem cells, immune evasion, and the complex tumor microenvironment. Cancer-associated fibroblasts and extracellular vesicles play crucial roles in modulating the tumor microenvironment and facilitating chemoresistance. EVs, naturally occurring nanovesicles, emerge as promising drug carriers due to their low toxicity, high biocompatibility, and inherent targeting capabilities. They have shown potential in delivering chemotherapeutics like doxorubicin, cisplatin, and paclitaxel, as well as natural compounds such as curcumin and berry anthocyanidins, enhancing therapeutic efficacy while reducing systemic toxicity in OC models. However, challenges such as low production yields, heterogeneity, rapid clearance, and inefficient drug loading methods need to be addressed for clinical application. Ongoing research aims to optimize EV production, loading efficiency, and targeting, paving the way for novel and more effective therapeutic strategies in OC treatment. Overcoming these obstacles is crucial to unlocking the full potential of EV-based therapies and improving outcomes for OC patients.

Multicompartmentalized microvascularized tumor-on-a-chip to study tumor-stroma interactions and drug resistance in ovarian cancer

Introduction

The majority of ovarian cancer (OC) patients receiving standard of care chemotherapy develop chemoresistance within 5 years. The tumor microenvironment (TME) is a dynamic and influential player in disease progression and therapeutic response. However, there is a lack of models that allow us to elucidate the compartmentalized nature of TME in a controllable, yet physiologically relevant manner and its critical role in modulating drug resistance.

Methods

We developed a 3D microvascularized multiniche tumor-on-a-chip formed by five chambers (central cancer chamber, flanked by two lateral stromal chambers and two external circulation chambers) to recapitulate OC-TME compartmentalization and study its influence on drug resistance. Stromal chambers included endothelial cells alone or cocultured with normal fibroblasts or cancer-associated fibroblasts (CAF).

Results

The tumor-on-a-chip recapitulated spatial TME compartmentalization including vessel-like structure, stromal-mediated extracellular matrix (ECM) remodeling, generation of oxygen gradients, and delayed drug diffusion/penetration from the circulation chamber towards the cancer chamber. The cancer chamber mimicked metastasis-like migration and increased drug resistance to carboplatin/paclitaxel treatment in the presence of CAF when compared to normal fibroblasts. CAF-mediated drug resistance was rescued by ECM targeted therapy. Critically, these results demonstrate that cellular crosstalk recreation and spatial organization through compartmentalization are essential to determining the effect of the compartmentalized OC-TME on drug resistance.

Conclusions

Our results present a functionally characterized microvascularized multiniche tumor-on-a-chip able to recapitulate TME compartmentalization influencing drug resistance. This technology holds the potential to guide the design of more effective and targeted therapeutic strategies to overcome chemoresistance in OC.

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.

Cutaneous Metastasis of Ovarian Cancer on 68 Ga-FAPI PET/CT

ABSTRACT

Ovarian cancer with cutaneous metastases is quite rare. We report the findings of cutaneous metastases from ovarian cancer on 68 Ga-FAPI PET/CT imaging. A 53-year-old woman with cutaneous metastases from ovarian cancer was enrolled in 68 Ga-FAPI PET/CT clinical trial. The images showed intense FAPI activity in the known cutaneous metastases.

Comprehensive single-cell analysis reveals heterogeneity of fibroblast subpopulations in ovarian cancer tissue microenvironment

Background

Ovarian cancer, as a highly malignant tumor, features the critical involvement of tumor-associated fibroblasts in the ovarian cancer tissue microenvironment. However, due to the apparent heterogeneity within fibroblast subpopulations, the specific functions of these subpopulations in the ovarian cancer tissue microenvironment remain insufficiently elucidated.

Methods

In this study, we integrated single-cell sequencing data from 32 ovarian cancer samples derived from four distinct cohorts and 3226 bulk RNA-seq data from GEO and TCGA-OV cohorts. Utilizing computational frameworks such as Seurat, Monocle 2, Cellchat, and others, we analyzed the characteristics of the ovarian cancer tissue microenvironment, focusing particularly on fibroblast subpopulations and their differentiation trajectories. Employing the CIBERSORTX computational framework, we assessed various cellular components within the ovarian cancer tissue microenvironment and evaluated their associations with ovarian cancer prognosis. Additionally, we conducted Mendelian randomization analysis based on cis-eQTL to investigate causal relationships between gene expression and ovarian cancer.

Results

Through integrative analysis, we identified 13 major cell types present in ovarian cancer tissues, including CD8+ T cells, malignant cells, and fibroblasts. Analysis of the tumor microenvironment (TME) cell proportions revealed a significant increase in the proportion of CD8+ T cells and CD4+ T cells in tumor tissues compared to normal tissues, while fibroblasts predominated in normal tissues. Further subgroup analysis of fibroblasts identified seven subgroups, with the MMP11+Fib subgroup showing the highest activity in the TGFβ signaling pathway. Single-cell analysis suggested that oxidative phosphorylation could be a key pathway driving fibroblast differentiation, and the ATRNL1+KCN + Fib subgroup exhibited chromosomal copy number variations. Prognostic analysis using a large sample size indicated that high infiltration of MMP11+ fibroblasts was associated with poor prognosis in ovarian cancer. SMR analysis identified 132 fibroblast differentiation-related genes, which were linked to pathways such as platinum drug resistance.

Conclusions

In the context of ovarian cancer, fibroblasts expressing MMP11 emerge as the primary drivers of the TGF-beta signaling pathway. Their presence correlates with an increased risk of adverse ovarian prognoses. Additionally, the genetic regulation governing the differentiation of fibroblasts associated with ovarian cancer correlates with the emergence of drug resistance.

Cancer-associated fibroblast-secreted FGF7 as an ovarian cancer progression promoter

BACKGROUND

Ovarian cancer (OC) is distinguished by its aggressive nature and the limited efficacy of current treatment strategies. Recent studies have emphasized the significant role of cancer-associated fibroblasts (CAFs) in OC development and progression.

METHODS

Employing sophisticated machine learning techniques on bulk transcriptomic datasets, we identified fibroblast growth factor 7 (FGF7), derived from CAFs, as a potential oncogenic factor. We investigated the relationship between FGF7 expression and various clinical parameters. A series of in vitro experiments were undertaken to evaluate the effect of CAFs-derived FGF7 on OC cell activities, such as proliferation, migration, and invasion. Single-cell transcriptomic analysis was also conducted to elucidate the interaction between FGF7 and its receptor. Detailed mechanistic investigations sought to clarify the pathways through which FGF7 fosters OC progression.

RESULTS

Our findings indicate that higher FGF7 levels correlate with advanced tumor stages, increased vascular invasion, and poorer prognosis. CAFs-derived FGF7 significantly enhanced OC cell proliferation, migration, and invasion. Single-cell analysis and in vitro studies revealed that CAFs-derived FGF7 inhibits the ubiquitination and degradation of hypoxia-inducible factor 1 alpha (HIF-1α) via FGFR2 interaction. Activation of the FGF7/HIF-1α pathway resulted in the upregulation of mesenchymal markers and downregulation of epithelial markers. Importantly, in vivo treatment with neutralizing antibodies targeting CAFs-derived FGF7 substantially reduced tumor growth.

CONCLUSION

Neutralizing FGF7 in the medium or inhibiting HIF-1α signaling reversed the effects of FGF7-mediated EMT, emphasizing the dependence of FGF7-mediated EMT on HIF-1α activation. These findings suggest that targeting the FGF7/HIF-1α/EMT axis may offer new therapeutic opportunities to intervene in OC progression.

Single-cell transcriptome analysis of epithelial, immune, and stromal signatures and interactions in human ovarian cancer

A comprehensive investigation of ovarian cancer (OC) progression at the single-cell level is crucial for enhancing our understanding of the disease, as well as for the development of better diagnoses and treatments. Here, over half a million single-cell transcriptome data were collected from 84 OC patients across all clinical stages. Through integrative analysis, we identified heterogeneous epithelial-immune-stromal cellular compartments and their interactions in the OC microenvironment. The epithelial cells displayed clinical subtype features with functional variance. A significant increase in distinct T cell subtypes was identified including Tregs and CD8+ exhausted T cells from stage IC2. Additionally, we discovered antigen-presenting cancer-associated fibroblasts (CAFs), with myofibroblastic CAFs (myCAFs) exhibiting enriched extracellular matrix (ECM) functionality linked to tumor progression at stage IC2. Furthermore, the NECTIN2-TIGIT ligand-receptor pair was identified to mediate T cells communicating with epithelial, fibroblast, endothelial, and other cell types. Knock-out of NECTIN2 using CRISPR/Cas9 inhibited ovarian cancer cell (SKOV3) proliferation, and increased T cell proliferation when co-cultured. These findings shed light on the cellular compartments and functional aspects of OC, providing insights into the molecular mechanisms underlying stage IC2 and potential therapeutic strategies for OC.

Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.

Patient-derived organoid culture in epithelial ovarian cancers-Techniques, applications, and future perspectives

Epithelial ovarian cancer (EOC) is a heterogeneous disease composed of different cell types with different molecular aberrations. Traditional cell lines and mice models cannot recapitulate the human tumor biology and tumor microenvironment (TME). Patient-derived organoids (PDOs) are freshly derived from patients' tissues and are then cultured with extracellular matrix and conditioned medium. The high concordance of epigenetic, genomic, and proteomic landscapes between the parental tumors and PDOs suggests that PDOs can provide more reliable results in studying cancer biology, allowing high throughput drug screening, and identifying their associated signaling pathways and resistance mechanisms. However, despite having a heterogeneity of cells in PDOs, some cells in TME will be lost during the culture process. Next-generation organoids have been developed to circumvent some of the limitations. Genetically engineered organoids involving targeted gene editing can facilitate the understanding of tumorigenesis and drug response. Co-culture systems where PDOs are cultured with different cell components like immune cells can allow research using immunotherapy which is otherwise impossible in conventional cell lines. In this review, the limitations of the traditional in vitro and in vivo assays, the use of PDOs, the challenges including some tips and tricks of PDO generation in EOC, and the future perspectives, will be discussed.

Reanalysis of single-cell data reveals macrophage subsets associated with the immunotherapy response and prognosis of patients with endometrial cancer

Endometrial cancer (EC) is an aggressive gynecological malignancy with an increased incidence rate. The immune landscape crucially affects immunotherapy efficacy and prognosis in EC patients. Here, we characterized the distinct tumor microenvironment signatures of EC tumors by analyzing single-cell RNA sequencing data from Gene Expression Omnibus and bulk RNA sequencing data from The Cancer Genome Atlas, which were compared with normal endometrium. Three macrophage subsets were identified, and two of them showed tissue-specific distribution. One of the macrophage subsets was dominant in macrophages derived from EC and exhibited characteristic behaviors such as promoting tumor growth and metastasis. One of the other macrophage subsets was mainly found in normal endometrium and served functions related to antigen presentation. We also identified a macrophage subset that was found in both EC and normal endometrial tissue. However, the pathway and cellular cross-talk of this subset were completely different based on the respective origin, suggesting a tumor-related differentiation mechanism of macrophages. Additionally, the tumor-enriched macrophage subset was found to predict immunotherapy responses in EC. Notably, we selected six genes from macrophage subset markers that could predict the survival of EC patients, SCL8A1, TXN, ANXA5, CST3, CD74 and NANS, and constructed a prognostic signature. To verify the signature, we identified immunohistochemistry for the tumor samples of 83 EC patients based on the selected genes and further followed up with the survival of the patients. Our results provide strong evidence that the signature can effectively predict the prognosis of EC patients.

Insights into the tumor-stromal-immune cell metabolism cross talk in ovarian cancer

The ovarian cancer tumor microenvironment (TME) consists of a constellation of abundant cellular components, extracellular matrix, and soluble factors. Soluble factors, such as cytokines, chemokines, structural proteins, extracellular vesicles, and metabolites, are critical means of noncontact cellular communication acting as messengers to convey pro- or antitumorigenic signals. Vast advancements have been made in our understanding of how cancer cells adapt their metabolism to meet environmental demands and utilize these adaptations to promote survival, metastasis, and therapeutic resistance. The stromal TME contribution to this metabolic rewiring has been relatively underexplored, particularly in ovarian cancer. Thus, metabolic activity alterations in the TME hold promise for further study and potential therapeutic exploitation. In this review, we focus on the cellular components of the TME with emphasis on 1) metabolic signatures of ovarian cancer; 2) understanding the stromal cell network and their metabolic cross talk with tumor cells; and 3) how stromal and tumor cell metabolites alter intratumoral immune cell metabolism and function. Together, these elements provide insight into the metabolic influence of the TME and emphasize the importance of understanding how metabolic performance drives cancer progression.

Omental cancer-associated fibroblast-derived exosomes with low microRNA-29c-3p promote ovarian cancer peritoneal metastasis

Ovarian cancer (OC) is characterized by frequent widespread peritoneal metastasis. Cancer-associated fibroblasts (CAFs) represent a critical stromal component of metastatic niche and promote omentum metastasis in OC patients. However, the role of exosomes derived from omental CAFs in metastasis remains unclear. We isolated exosomes from primary omental normal fibroblasts (NFs) and CAFs from OC patients (NF-Exo and CAF-Exo, respectively) and assessed their effect on metastasis. In mice bearing orthotopic OC xenografts, CAF-Exo treatment led to more rapid intraperitoneal tumor dissemination and shorter animal survival. Similar results were observed in mice undergoing intraperitoneal injection of tumor cells. Among the miRNAs downregulated in CAF-Exo, miR-29c-3p in OC tissues was associated with metastasis and survival in patients. Moreover, increasing miR-29c-3p in CAF-Exo significantly weakened the metastasis-promoting effect of CAF-Exo. Based on RNA sequencing, expression assays, and luciferase assays, matrix metalloproteinase 2 (MMP2) was identified as a direct target of miR-29c-3p. These results verify the significant contribution of exosomes from omental CAFs to OC peritoneal metastasis, which could be partially due to the relief of MMP2 expression inhibition mediated by low exosomal miR-29c-3p.

Cisplatin in Ovarian Cancer Treatment-Known Limitations in Therapy Force New Solutions

Cisplatin is one of the most commonly used anticancer drugs worldwide. It is mainly used in the treatment of ovarian cancer, but also used in testicular, bladder and lung cancers. The significant advantage of this drug is the multidirectional mechanism of its anticancer action, with the most important direction being damaging the DNA of cancer cells. Unfortunately, cisplatin displays a number of serious disadvantages, including toxicity to the most important organs, such as kidneys, heart, liver and inner ear. Moreover, a significant problem among patients with ovarian cancer, treated with cisplatin, is the development of numerous resistance mechanisms during therapy, including changes in the processes of cellular drug import and export, changes in the DNA damage repair mechanisms, as well as numerous changes in the processes of apoptosis and autophagy. Due to all of the mentioned problems, strategies to increase the effectiveness of cisplatin in the treatment of ovarian cancer are intensively sought. The most important strategy includes the development of less toxic cisplatin analogs. Another important direction is combination therapy, involving the simultaneous use of cisplatin with different anticancer drugs, substances derived from plants, temperature or radiotherapy. Many years of observations accompanying the presence of cisplatin in the therapy made it possible to provide a series of verifiable, statistically significant data, but also to show how, over time, with the new information and scientific discoveries, it is possible to describe and understand the therapeutic problems observed in practice, such as the acquisition of drug resistance by tumor cells or induction of changes in the tumor microenvironment. According to the authors, confronting what we knew so far with what new trends offer has a profound meaning. This paper presents information on the history of cisplatin and describes the molecular mechanisms of its action and the development of resistance by cancer cells. In addition, our goal was to highlight a number of therapeutic strategies to increase the effectiveness of cisplatin in the treatment of ovarian cancer, as well as to identify methods to eliminate problems associated with the use of cisplatin.

Targeting Tumor Microenvironment Akt Signaling Represents a Potential Therapeutic Strategy for Aggressive Thyroid Cancer

Effects of the tumor microenvironment (TME) stromal cells on progression in thyroid cancer are largely unexplored. Elucidating the effects and underlying mechanisms may facilitate the development of targeting therapy for aggressive cases of this disease. In this study, we investigated the impact of TME stromal cells on cancer stem-like cells (CSCs) in patient-relevant contexts where applying in vitro assays and xenograft models uncovered contributions of TME stromal cells to thyroid cancer progression. We found that TME stromal cells can enhance CSC self-renewal and invasiveness mainly via the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathway. The disruption of Akt signaling could diminish the impact of TME stromal cells on CSC aggressiveness in vitro and reduce CSC tumorigenesis and metastasis in xenografts. Notably, disrupting Akt signaling did not cause detectable alterations in tumor histology and gene expression of major stromal components while it produced therapeutic benefits. In addition, using a clinical cohort, we discovered that papillary thyroid carcinomas with lymph node metastasis are more likely to have elevated Akt signaling compared with the ones without metastasis, suggesting the relevance of Akt-targeting. Overall, our results identify PI3K/Akt pathway-engaged contributions of TME stromal cells to thyroid tumor disease progression, illuminating TME Akt signaling as a therapeutic target in aggressive thyroid cancer.

Ovarian Cancer—Insights into Platinum Resistance and Overcoming It

Ovarian cancer is the most lethal gynecologic malignancy. Platinum-based chemotherapy is the backbone of treatment for ovarian cancer, and although the majority of patients initially have a platinum-sensitive disease, through multiple recurrences, they will acquire resistance. Platinum-resistant recurrent ovarian cancer has a poor prognosis and few treatment options with limited efficacy. Resistance to platinum compounds is a complex process involving multiple mechanisms pertaining not only to the tumoral cell but also to the tumoral microenvironment. In this review, we discuss the molecular mechanism involved in ovarian cancer cells’ resistance to platinum-based chemotherapy, focusing on the alteration of drug influx and efflux pathways, DNA repair, the dysregulation of epigenetic modulation, and the involvement of the tumoral microenvironment in the acquisition of the platinum-resistant phenotype. Furthermore, we review promising alternative treatment approaches that may improve these patients’ poor prognosis, discussing current strategies, novel combinations, and therapeutic agents.

A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is the most fatal gynaecological malignancy and has a poor prognosis. Glycosylation, the biosynthetic process that depends on specific glycosyltransferases (GTs), has recently attracted increasing importance due to the vital role it plays in cancer. In this study, we aimed to determine whether OC patients could be stratified by glycosyltransferase gene profiles to better predict the prognosis and efficiency of immune checkpoint blockade therapies (ICBs).

METHODS

We retrieved transcriptome data across 420 OC and 88 normal tissue samples using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively. An external validation dataset containing 185 OC samples was downloaded from the Gene Expression Omnibus (GEO) database. Knockdown and pathway prediction of B4GALT5 were conducted to investigate the function and mechanism of B4GALT5 in OC proliferation, migration and invasion.

RESULTS

A total of 50 differentially expressed GT genes were identified between OC and normal ovarian tissues. Two clusters were stratified by operating consensus clustering, but no significant prognostic value was observed. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 6-gene signature was built that classified OC patients in the TCGA cohort into a low- or high-risk group. Patients with high scores had a worse prognosis than those with low scores. This risk signature was further validated in an external GEO dataset. Furthermore, the risk score was an independent risk predictor, and a nomogram was created to improve the accuracy of prognostic classification. Notably, the low-risk OC patients exhibited a higher degree of antitumor immune cell infiltration and a superior response to ICBs. B4GALT5, one of six hub genes, was identified as a regulator of proliferation, migration and invasion in OC.

CONCLUSION

Taken together, we established a reliable GT-gene-based signature to predict prognosis, immune status and identify OC patients who would benefit from ICBs. GT genes might be a promising biomarker for OC progression and a potential therapeutic target for OC.

Patient-Derived Primary Cancer-Associated Fibroblasts Mediate Resistance to Anti-Angiogenic Drug in Ovarian Cancers

Ovarian cancers rank first in both aggressiveness and dismal prognosis among gynecological neoplasms. The poor outcome is explained by the fact that most patients present with late-stage disease and progress through the first line of treatment. Ovarian neoplasms, especially epithelial ovarian cancers, are diagnosed at advanced/metastatic stages, often with a high angiogenesis index, one of the hallmarks of ovarian cancers with rapid progression and poor outcome as resistance to anti-angiogenic therapy develops. Despite therapy, the metastatic progression of aggressive ovarian cancer is a spectacularly selective function of tumor cells aided and abetted by the immune, mesenchymal and angiogenic components of the tumor microenvironment (TME) that enforces several pro-metastatic event(s) via direct and indirect interactions with stromal immune cells, cancer-associated fibroblasts (CAFs), and vascular endothelial cells. Since transdifferentiation of tumor endothelium is one of the major sources of CAFs, we hypothesized that ovarian CAF plays a critical role in resisting anti-angiogenic effects via direct crosstalk with endothelium and hence plays a direct role in the development of resistance to anti-angiogenic drugs. To test the hypothesis, we set up a hybrid ex vivo model for co-culture comprising Patient-Derived ex vivo primary CAFs from ovarian tumor samples and human umbilical vein endothelial cells (HUVEC). Patient-Derived CAFs were characterized by the mRNA and protein expression of positive (SMA, S100A4, TE-7, FAP-A, CD90/THY1), negative (EpCAM, CK 8,18, CD31, CD44, CD45), functional (PDGFRA, TGFB1, TGFB2, TGFRA) and immunological markers (PD-L1, PD-L2, PD-1) associated with CAFs by qRT-PCR, flow cytometry, Western blot, and ICC. Data from our HUVEC-on-CAF ex vivo Hybrid Co-Culture (HyCC) study demonstrate the pro-angiogenic effect of Patient-Derived ovarian CAFs by virtue of their ability to resist the effect of anti-angiogenic drugs, thereby aiding the development of resistance to anti-angiogenic drugs. Ascertaining direct experimental proof of the role of CAFs in developing resistance to specific anti-angiogenic drugs will provide an opportunity to investigate new drugs for counteracting CAF resistance and "normalizing/re-educating" TME in aggressive ovarian cancers. Our data provide a unique experimental tool for the personalized testing of anti-angiogenic drugs, positively predicting the development of future resistance to anti-angiogenic drugs well before it is clinically encountered in patients.

Identification of necroptosis subtypes and development of necroptosis-related risk score model for in ovarian cancer

Background: ith the ongoing development of targeted therapy, non-apoptotic cell death, including necroptosis, has become a popular topic in the field of prevention and treatment. The purpose of this study was to explore the effect of necroptosis-related genes (NRGs) on the classification of ovarian cancer (OV) subtypes and to develop a necroptosis-related risk score (NRRS) classification system. Methods: 74 NRGs were obtained from the published studies, and univariate COX regression analysis was carried out between them and OV survival. Consensus clustering analysis was performed on OV samples according to the expression of NRGs related to prognosis. Furthermore, the NRRS model was developed by combining Weighted Gene Co-Expression Network Analysis (WGCNA) with least absolute shrinkage and selection operator (Lasso)-penalized Cox regression and multivariate Cox regression analysis. And the decision tree model was constructed based on the principle of random forest screening factors principle. Results: According to the post-related NRGs, OV was divided into two necroptosis subtypes. Compared with Cluster 1 (C1), the overall survival (OS) of Cluster 2 (C2) was significantly shorter, stromal score and immune score, the infiltration level of tumor associated immune cells and the expression of 20 immune checkpoints were significantly higher. WGCNA identified the blue module most related to necroptosis subtype, and 12 genes in the module were used to construct NRRS. NRRS was an independent prognostic variable of OV. The OS of samples with lower NRRS was significantly longer, and tumor mutation burden and homologous recombination defect were more obvious. Conclusion: This study showed that necroptosis plays an important role in the classification, prognosis, immune infiltration and biological characteristics of OV subtypes. The evaluation of tumor necroptosis may provide a new perspective for OV treatment.

Modulating the tumor immune microenvironment with nanoparticles: A sword for improving the efficiency of ovarian cancer immunotherapy

With encouraging antitumor effects, immunotherapy represented by immune checkpoint blockade has developed into a mainstream cancer therapeutic modality. However, only a minority of ovarian cancer (OC) patients could benefit from immunotherapy. The main reason is that most OC harbor a suppressive tumor immune microenvironment (TIME). Emerging studies suggest that M2 tumor-associated macrophages (TAMs), T regulatory cells (Tregs), myeloid-derived suppressor cells (MDSCs), and cancer-associated fibroblasts (CAFs) are enriched in OC. Thus, reversing the suppressive TIME is considered an ideal candidate for improving the efficiency of immunotherapy. Nanoparticles encapsulating immunoregulatory agents can regulate immunocytes and improve the TIME to boost the antitumor immune response. In addition, some nanoparticle-mediated photodynamic and photothermal therapy can directly kill tumor cells and induce tumor immunogenic cell death to activate antigen-presenting cells and promote T cell infiltration. These advantages make nanoparticles promising candidates for modulating the TIME and improving OC immunotherapy. In this review, we analyzed the composition and function of the TIME in OC and summarized the current clinical progress of OC immunotherapy. Then, we expounded on the promising advances in nanomaterial-mediated immunotherapy for modulating the TIME in OC. Finally, we discussed the obstacles and challenges in the clinical translation of this novel combination treatment regimen. We believe this resourceful strategy will open the door to effective immunotherapy of OC and benefit numerous patients.

Integration of local and systemic immunity in ovarian cancer: Implications for immunotherapy

Cancer is a disease that induces many local and systemic changes in immunity. The difficult nature of ovarian cancer stems from the lack of characteristic symptoms that contributes to a delayed diagnosis and treatment. Despite the enormous progress in immunotherapy, its efficacy remains limited. The heterogeneity of tumors, lack of diagnostic biomarkers, and complex immune landscape are the main challenges in the treatment of ovarian cancer. Integrative approaches that combine the tumor microenvironment - local immunity - together with periphery - systemic immunity - are urgently needed to improve the understanding of the disease and the efficacy of treatment. In fact, multiparametric analyses are poised to improve our understanding of ovarian tumor immunology. We outline an integrative approach including local and systemic immunity in ovarian cancer. Understanding the nature of both localized and systemic immune responses will be crucial to boosting the efficacy of immunotherapies in ovarian cancer patients.

NR2F1 Regulates TGF-β1-Mediated Epithelial-Mesenchymal Transition Affecting Platinum Sensitivity and Immune Response in Ovarian Cancer

The mechanism underlying platinum resistance in ovarian cancer (OC) remains unclear. We used bioinformatic analyses to screen differentially expressed genes responsible for platinum resistance and explore NR2F1′s correlation with prognostic implication and OC staging. Moreover, Gene-set enrichment analysis (GSEA) and Gene Ontology (GO) analyses were used for pathway analysis. Epithelial-mesenchymal transition (EMT) properties, invasion, and migration capacities were analyzed by biochemical methods. The association between NR2F1 and cancer-associated fibroblast (CAF) infiltration and immunotherapeutic responses were also researched. A total of 13 co-upregulated genes and one co-downregulated gene were obtained. Among them, NR2F1 revealed the highest correlation with a poor prognosis and positively correlated with OC staging. GSEA and GO analysis suggested the induction of EMT via TGFβ-1 might be a possible mechanism that NR2F1 participates in resistance. In vitro experiments showed that NR2F1 knockdown did not affect cell proliferation, but suppressed cell invasion and migration with or without cisplatin treatment through the EMT pathway. We also found that NR2F1 could regulate TGF-β1 signaling, and treating with TGF-β1 could reverse these effects. Additionally, NR2F1 was predominantly associated with immunosuppressive CAF infiltration, which might cause a poor response to immune check blockades. In conclusion, NR2F1 regulates TGF-β1-mediated EMT affecting platinum sensitivity and immune response in OC patients.