Immunotherapy Of Ovarian Cancer Research Articles

Transcription factor networks and novel immune biomarkers reveal key prognostic and therapeutic insights in ovarian cancer

BackgroundUnderstanding the tumor microenvironment (TME) is essential for the advancement of immunotherapy for ovarian cancer (OC). Nonetheless, predicting transcription factor (TF) regulation from the TME using single-cell RNA sequencing (scRNA-seq) data is challenging.MethodsThe OC scRNA-seq data were analyzed with a specialized scRNA-seq transcriptome analysis application. The OC TME was utilized to instruct the SCENIC procedure for TF regulation. We built a risk model using Lasso regression and identified immunological subgroups using ConsensusClusterPlus. To analyze the percentage of invading immune cells, the algorithms CIBERSORT, ESTIMATE, and xCell were used. We computed the stromal score, immunological score, estimate score, and tumor purity to evaluate the risk model's capacity to predict the tumor immune microenvironment. Additionally, the expression of immunological checkpoints was examined, and for pertinent evaluation, the imvigor 210 dataset of the immunotherapy cohort was used. pRophetic predicted the sensitivity of 138 GDSC database drugs. In addition, we examined the expression of unproven risk model genes using qPCR and immunohistochemistry (JCHAIN, UBD, and RARRES1). Cell proliferation was assessed by colony formation assays. Transwell experiments were used to examine the invasion and migration ability of OC cells.ResultsSix immunologically malignant cell subpopulations have been identified within the cancer immune microenvironment (referred to as TC0-6). Unique in its immunological profile, TC0 demonstrates the most intimate interactions with immune cells. Following a meta gene screen in the TC0 subpopulation using the top 30 targets of 14 transcription factor (TF) factors, two distinct immunological molecular subtypes—the C1 and C2 subtypes—with notable survival differences were discovered. On the basis of nine genes whose expression differs between the C1 and C2 subtypes, a risk model was constructed. The risk model is an accurate method for forecasting the effectiveness of immunotherapies, clinicopathological characteristics, and survival. JCHAIN and UBD expression in OC tissues was found to be low according to qPCR and IHC analyses, whereas RARRES1 expression was found to be high. The functional experiment results indicated that downregulation of JCHAIN and UBD and overexpression of RARRES1 could suppress the proliferation, migration, and invasion of OC cells in vitro.ConclusionBased on TF regulatory networks in the tumor microenvironment, this study developed a 9-gene risk model for the prognosis of ovarian cancer. This model may aid in the future promotion of personalized OC immunotherapy. In addition, JCHAIN, UBD, and RARRES1 were identified as three novel immune-related biomarkers for OC.

In vitro ovarian tumor-conditioned CD163+ human macrophages retain phagocytic response to CD47 blockade.

In vitro ovarian tumor-conditioned CD163+ human macrophages retain phagocytic response to CD47 blockade.

Unlocking the future: Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response

BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer (OC) and affect immune cell infiltration and treatment responses. AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks. METHODS Prognosis, immunotherapy efficacy, and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Mitochondrial genes were sourced from the MitoCarta3.0 database. The discovery cohort for model construction was created from 70% of the patients, whereas the remaining 30% constituted the validation cohort. Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm, the overall survival time and immunotherapy efficacy (complete or partial response) of patients were predicted. RESULTS In total, 375 patients with OC were included to construct the prognostic model, and 26 patients were included to construct the immune efficacy model. The average area under the receiver operating characteristic curve of the prognostic model was 0.7268 [95% confidence interval (CI): 0.7258-0.7278] in the discovery cohort and 0.6475 (95%CI: 0.6466-0.6484) in the validation cohort. The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444 (95%CI: 0.8333-1.0000) in the discovery cohort and 0.9167 (95%CI: 0.6667-1.0000) in the validation cohort. CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC, providing valuable insights into personalized treatment strategies.

Activating the cGAS-STING Pathway by Manganese-Based Nanoparticles Combined with Platinum-Based Nanoparticles for Enhanced Ovarian Cancer Immunotherapy.

Recent research has demonstrated that activating the cGAS-STING pathway can enhance interferon production and the activation of T cells. A manganese complex, called TPA-Mn, was developed in this context. The reactive oxygen species (ROS)-sensitive nanoparticles (NPMn) loaded with TPA-Mn are developed. NPMn activates the cGAS-STING pathway via cGAS activation (i.e., 1.6-fold enhancement of P-STING), which in turn increases the secretion of pro-inflammatory cytokines (e.g., TNF-α, IL-6, and IL-2). This promotes dendritic cell maturation, enhances the infiltration of cytotoxic T lymphocytes, and reduces the percentage of immunosuppressive regulatory T cells. In addition, it is crucial to emphasize that cisplatin-induced DNA damage can potentially trigger activation of the cGAS-STING pathway. NPMn, in combination with low-dose NPPt, a carrier of a Cis(IV) prodrug capable of causing DNA damage, augments the cGAS-STING pathway activation and significantly activates the tumor immune microenvironment (TIME). Furthermore, combined with anti-PD-1 antibody, NPPt+NPMn shows synergistic efficacy in both ovarian cancer peritoneal metastases and recurrence models. It not only effectively eliminates tumors but also induces a strong immune memory response, providing a promising strategy for the clinical management of ovarian cancer. This work offers a design of manganese-based nanoparticles for immunotherapy.

Immunotherapy in Recurrent Ovarian Cancer.

It remains challenging to treat recurrent ovarian cancer effectively as traditional interventions like chemotherapy and surgery have limited long-term efficacy, highlighting an urgent need for innovative approaches. Immunotherapy offers potential advantages in modulating the immune response against tumor cells and has emerged as a promising strategy in ovarian cancer management. This review discusses various immunotherapy modalities, including active and passive immune strategies, for recurrent ovarian cancer. We systematically reviewed recent immunotherapy advances for recurrent ovarian cancer, including the efficacy and mechanisms of single and dual immune checkpoint inhibitors, checkpoint inhibitor combinations with chemotherapy or radiotherapy, anti-angiogenic agents, PARP inhibitors, antibody-drug conjugates (ADC), tumor vaccines, and adoptive cell therapies (ACT). Additionally, we assessed emerging research on biomarkers predictive of immunotherapy responsiveness in ovarian cancer. The findings indicate that immunotherapy, particularly combinations involving immune checkpoint inhibitors and other agents, demonstrates promising efficacy in recurrent ovarian cancer, with some therapies showing enhanced benefits in specific subtypes. The immune microenvironment in platinum-sensitive and -resistant cases exhibits distinct immunological profiles, influencing therapy outcomes. Several potential biomarkers have been identified, potentially aiding in patient stratification and treatment optimization. Immunotherapy significantly advances recurrent ovarian cancer treatment, with various combinations potentially improving outcomes. Further research on predictive biomarkers and immune microenvironment characteristics is crucial for personalizing immunotherapy approaches and enhancing their efficacy in managing recurrent ovarian cancer.

Unlocking the future: mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response.

Mitochondrial genes are involved in the tumor metabolism of ovarian cancer (OC), affecting immune cell infiltration and treatment response. We aimed to utilize mitochondrial genes to predict OC prognosis and immunotherapy response. The prognosis data, immunotherapy efficacy and next generation sequencing data of OC patients were downloaded from The Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO). Mitochondrial genes were sourced from the MitoCarta3.0 database. Seventy percent of the patients were randomly selected as the discovery cohort for model construction, while the remaining 30% constituted the validation cohort for model assessment. Using the expression of mitochondrial genes as the predictor variable and based on the neural network algorithm, the overall survival (OS) time and immunotherapy efficacy (complete or partial response) of the included patients were predicted. There were 375 OC patients included to construct the prognostic model, and 26 patients were included to construct the immune efficacy model. The average area under the receiver operating characteristic curve (AUC) of the prognostic model was: 0.7268 [95% confidence interval (CI), 0.7258-0.7278] in the discovery cohort and 0.6475 (95% CI: 0.6466-0.6484) in the validation cohort. The average AUC of the immunotherapy efficacy model was: 0.9444 (95% CI: 0.8333-1.0000) in the discovery cohort and 0.9167 (95% CI: 0.6667-1.0000) in the validation cohort. The application of mitochondrial genes and neural networks shows potential in predicting the prognosis and immunotherapy response in OC patients. And this approach could provide valuable insights for personalized treatment strategies.

Transcriptome analysis of ovarian cancer uncovers association between tumor-related inflammation/immunity and patient outcome.

Epithelial ovarian cancer (EOC) is a cancer that affects the female reproductive system and is highly lethal. It poses significant challenges in terms of treatment and often has a poor prognosis. In recent years, with the advent of PARPi, the treatment of ovarian cancer has entered a new stage of full-process management. Although more and more drugs have been approved, the therapeutic effect of PARPi is still very limited. With the rapid development of PD-1/PD-L1, CTLA-4, oncolytic viruses, cancer vaccines, adoptive cell therapy, etc., tumor immunotherapy has provided new opportunities for the treatment of ovarian cancer. This study used comprehensive transcriptome analysis across multiple databases to gather gene transcripts and clinical features of normal ovarian samples and tissue samples from ovarian cancer. The aim was to explore the mechanisms underlying tumor immunotherapy resistance and to reveal the relationship between ovarian cancer's immune microenvironment and genes linked to inflammation. Various R packages were used for differential gene analysis, enrichment analysis, co-expression network construction, and prognostic model building. It has been found that the prognosis of ovarian cancer patients is closely associated with sets of genes involved in inflammation. The immune infiltration microenvironment, clinicopathological features, and survival rates differed significantly between two inflammatory gene expression patterns identified using cluster and immune microenvironment analyses. Further analysis revealed that the high-risk group had a higher abundance of M2-type macrophage infiltration, more active anti-tumor immune response, higher tumor stemness score, potentially worse prognosis, and lower response rates to multiple chemotherapy drugs and immune checkpoint inhibitors. These findings provide new perspectives and potential targets for immunotherapy and prognostic evaluation of ovarian cancer and offer new strategies and directions for clinical treatment and patient management. This study provides crucial information to further our comprehension of drug response mechanisms and tumor immunotherapy. It offers new strategies and methods for the treatment and prognostic improvement of ovarian cancer.

Dendritic cell immunotherapy and topotecan-based metronomic chemotherapy in ovarian cancer — a case study and literature review

Dendritic cell immunotherapy and topotecan-based metronomic chemotherapy in ovarian cancer — a case study and literature review

Shining a Spotlight on RNA m6A Modifications and Potential Targeted Immunotherapy in Ovarian Cancer

Objective: Ovarian cancer is one of the major gynecologic malignancies worldwide. Evidence indicates that N6-methyladenosine (m6A) modification is strongly related to cancer immunity. This review aims to demonstrate the role and function of m6A modification in ovarian cancer tumorigenesis and its potential targeted immunotherapy. Mechanism: Searching the literature for articles related to the m6A modification and ovarian cancer and immunotherapy. Conducting a comprehensive analysis and summarizing the content logically. Findings in Brief: In this review, we summarized the latest research on the function of m6A regulators in ovarian cancer tumorigenesis and targeted immunotherapy. Firstly, we found the function and underlying molecular mechanism of m6A regulators (writers, erasers and readers) in the development and progress of ovarian cancer. Then, we discovered that m6A regulators play a pivotal role in cancer immune responses and efficacy, serving as potential therapeutic targets for cancer immunotherapy. Additionally, m6A related long noncoding RNAs (lncRNAs) have a strong impact on ovarian cancer survival and immunotherapy. Finally, deeply analyzing of m6A modification and targeted genes are needed to improve the potential immunotherapy of ovarian cancer. Conclusions: m6A modification has strong potential in ovarian cancer carcinogenesis and immunotherapy.

Comprehensive analysis of the value of angiogenesis and stemness-related genes in the prognosis and immunotherapy of ovarian cancer.

Tumor angiogenesis and the presence of cancer stem cells (CSCs) are critical characteristics of tumors. Previous research has demonstrated that cancer stem cells promote tumor angiogenesis, while increased vascularity, in turn, fosters the growth of cancer stem cells. This creates a detrimental cycle that contributes to tumor progression. However, studies investigating the angiogenesis and stemness characteristics in ovarian cancer (OV) are limited. In this study, we employed cluster analysis and LASSO methods to assess the significance of angiogenesis- and stemness-related genes in the efficacy of OV immunotherapy. Through multivariate Cox regression analysis and Friends analysis, we identified TNFSF11 as the most significant prognostic gene associated with angiogenesis and stemness. Additionally, molecular docking results confirmed that TNFSF11 exhibits a high affinity for sorafenib and sunitinib. In summary, for the first time, we conducted a comprehensive analysis of the roles of angiogenesis and stemness-related genes in the prognosis and immunotherapy of OV patients, revealing TNFSF11 as a novel therapeutic target.

TIGIT+ CD4+ regulatory T cells enhance PD-1 expression on CD8+ T cells and promote tumor growth in a murine ovarian cancer model

Immune checkpoint-based immunotherapy has shown limited efficacy in the treatment of ovarian cancer. In recent years, the emergence of immune checkpoint co-targeting therapies, led by the combination targeting of TIGIT and FAK, has shown promise in ovarian cancer treatment. Our preliminary research indicates that TIGIT is predominantly expressed in regulatory T cells during ovarian cancer. However, the therapeutic impact of TIGIT targeting based on regulatory T cells in ovarian cancer remains to be elucidated. We utilized ID8 cells to establish a mouse model of ovarian cancer. Through flow cytometry and co-culture methods, we validated the relationship between the functionality of regulatory T cells and tumor masses, and confirmed the crucial role of TIGIT in immune suppression in ovarian cancer. Furthermore, using Foxp3-diphtheria toxin receptor (DTR) mice, we substantiated that the combined TIGIT antibody treatment, based on targeting regulatory T cells, effectively slowed down the progression of ovarian cancer. Taken together, our results have demonstrated that dual targeting of regulatory T cells and TIGIT effectively retards tumor growth, laying the groundwork for the clinical application of immune checkpoint combination therapies. Future research in ovarian cancer immunotherapy is leaning towards a strategy that combines multiple targets, and specific cell-type immunotherapies.

Integrated immunogenomic analyses of high-grade serous ovarian cancer reveal vulnerability to combination immunotherapy.

The efficacy of immunotherapies in high-grade serous ovarian cancer (HGSOC) is limited, but clinical trials investigating the potential of combination immunotherapy including poly-ADP-ribose polymerase inhibitors (PARPis) are ongoing. Homologous recombination repair deficiency or BRCAness and the composition of the tumor microenvironment appear to play a critical role in determining the therapeutic response. We conducted comprehensive immunogenomic analyses of HGSOC using data from several patient cohorts. Machine learning methods were used to develop a classification model for BRCAness from gene expression data. Integrated analysis of bulk and single-cell RNA sequencing data was used to delineate the tumor immune microenvironment and was validated by immunohistochemistry. The impact of PARPi and BRCA1 mutations on the activation of immune-related pathways was studied using ovarian cancer cell lines, RNA sequencing, and immunofluorescence analysis. We identified a 24-gene signature that predicts BRCAness. Comprehensive immunogenomic analyses across patient cohorts identified samples with BRCAness and high immune infiltration. Further characterization of these samples revealed increased infiltration of immunosuppressive cells, including tumor-associated macrophages expressing TREM2, C1QA, and LILRB4, as specified by single-cell RNA sequencing data and gene expression analysis of samples from patients receiving combination therapy with PARPi and anti-PD-1. Our findings show also that genomic instability and PARPi activated the cGAS-STING signaling pathway in vitro and the downstream innate immune response in a similar manner to HGSOC patients with BRCAness status. Finally, we have developed a web application (https://ovrseq.icbi.at) and an associated R package OvRSeq, which allow for comprehensive characterization of ovarian cancer patient samples and assessment of a vulnerability score that enables stratification of patients to predict response to the combination immunotherapy. Genomic instability in HGSOC affects the tumor immune environment, and TAMs play a crucial role in modulating the immune response. Based on various datasets, we have developed a diagnostic application that uses RNA sequencing data not only to comprehensively characterize HGSOC but also to predict vulnerability and response to combination immunotherapy.

Immunomodulating Sonocatalytic Nanoagents with Dual‐Functional Ir‐N Centers and Narrow Bandgap for Reversing Immunosuppression and Potentiating Ovarian Cancer Immunotherapy (Adv. Funct. Mater. 46/2024)

Immunomodulating Sonocatalytic Nanoagents with Dual‐Functional Ir‐N Centers and Narrow Bandgap for Reversing Immunosuppression and Potentiating Ovarian Cancer Immunotherapy (Adv. Funct. Mater. 46/2024)

Unraveling the potential biomarkers of immune checkpoint inhibitors in advanced ovarian cancer: a comprehensive review.

The ongoing research on the role of immunotherapy in advanced ovarian cancer (OC) and current clinical trials indicate that patients shown limited response to immune checkpoint inhibitor (ICI) monotherapy. When combined with other treatments or drugs, the efficacy of immunotherapy will be significantly improved. Biomarkers can be used to identify patients with better responses, thereby improving the precision and efficacy of immunotherapy. Key biomarkers for advanced OC include homologous repair deficiency, programmed death-ligand (PD-L) 1 expression, chemokines, and tumor infiltrating lymphocytes. These biomarkers could be applied in the future to select the most suitable patient populations. This review comprehensively examines the research and development of biomarkers in OC immunotherapy from three omics perspectives: genomics, transcriptomics, and proteomics, which may provide guidance for the effectiveness of OC immunotherapy strategies.

Unlocking the potential of immunotherapy in platinum-resistant ovarian cancer: rationale, challenges, and novel strategies.

Ovarian cancer is a significant global health challenge, with cytoreductive surgery and platinum-based chemotherapy serving as established primary treatments. Unfortunately, most patients relapse and ultimately become platinum-resistant, at which point there are limited effective treatment options. Given the success of immunotherapy in inducing durable treatment responses in several other cancers, its potential in platinum-resistant ovarian cancer (PROC) is currently being investigated. However, in unselected advanced ovarian cancer populations, researchers have reported low response rates to immune checkpoint inhibition, and thus far, no validated biomarkers are predictive of response. Understanding the intricate interplay between platinum resistance, immune recognition, and the tumour microenvironment (TME) is crucial. In this review, we examine the research challenges encountered thus far, the biological rationale for immunotherapy, the underlying mechanisms of immune resistance, and new strategies to overcome resistance.

Dysgerminomas: germ cell tumors exhibit high expression of PD-L1 and associated with high TILs and good prognosis

Ovarian germ cell tumors (OVGCTs) account for 28% of all diagnosed ovarian cancers, and malignant germ cell tumors specifically account for approximately 13% of diagnosed ovarian cancers in Saudi Arabia. Although most germ cell tumor patients have a high survival rate, patients who experience tumor recurrence have a poor prognosis and present with more aggressive and chemoresistant tumors. The use of immunotherapeutic agents such as PD-L1/PD-1 inhibitors for OVGCTs remains very limited because few studies have described the immunological characteristics of these tumors. This study is the first to investigate PD-L1 expression in ovarian germ cell tumors and explore the role of PD-L1 expression in tumor microenvironment cells and genetic alterations. A total of 34 ovarian germ cell tumors were collected from pathology archives. The collected tumor tissues included ten dysgerminomas, five yolk sac tumors, five immature teratomas, and one mature teratoma, and the remaining samples were mixed germ cell tumors. The tumors were analyzed using immunohistochemical analysis to determine PD-L1 expression, immune cell infiltration and cancer stem cell populations and their correlation with clinical outcome. Furthermore, the genetic alterations in different subtypes of germ cell tumors were correlated with PD-L1 expression and clinical outcome. Datasets for testicular germ cells (TGCTs) were retrieved from The Cancer Genome Atlas (TCGA) and analyzed using cBioPortal (cbioportal.org) and Gene Expression Profiling Interactive Analysis (GEPIA). Compared with yolk sac tumors, dysgerminomas highly express PD-L1 and are associated with high levels of tumor infiltrating lymphocytes (TILs) and stem cell markers. In addition, compared with PD-L1-negative yolk sac tissue, dysgerminomas/seminomas with high PD-L1 expression are associated with more genetic alterations and a better prognosis. Our findings will contribute to the knowledge about the potential benefits of ovarian cancer immunotherapy in specific subsets of germ cell tumor patients and the risk factors for resistance mediated by tumor microenvironment cells.

Investigating PPT2’s role in ovarian cancer prognosis and immunotherapy outcomes

Ovarian cancer (OC) remains the primary cause of mortality among gynecological malignancies, and the identification of reliable molecular biomarkers to prognosticate OC outcomes is yet to be achieved. The gene palmitoyl protein thioesterase 2 (PPT2), which has been sparsely studied in OC, was closely associated with metabolism. This study aimed to determine the association between PPT2 expression, prognosis, immune infiltration, and potential molecular mechanisms in OC. We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO) databases, then Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to assess and verify the role of PPT2. Gene set enrichment analysis (GSEA) was used to figure out the closely correlated pathways with PPT2. Overexpression experiment was performed to explore the function of PPT2. Our findings showed that PPT2 mRNA expression was apparent down-regulation in OC tissue compared to normal ovarian tissues in TCGA, GTEx datasets, and GEO datasets. This differential expression was also confirmed in our in-house datasets at both the mRNA and protein levels. Decreased PPT2 expression correlated with lower survival rates in TCGA, several GEO datasets, and our in-house datasets. Multivariate analysis revealed that PPT2 was an independent factor in predicting better outcomes for OC patients in TCGA and GEO. A negative correlation was revealed between immune infiltration and PPT2 expression through Single-sample GSEA (ssGSEA). Additionally, PPT2 was negatively correlated with an up-regulated immune score, stromal score, and estimate score, suggesting that patients with low PPT2 expression might benefit more from immunotherapy. Numerous chemical agents showed lower IC50 in patients with high PPT2 expression. In single-cell RNA sequencing (scRNA-seq) analysis of several OC datasets, we found PPT2 was mainly expressed in endothelial cells. Furthermore, we found that PPT2 inhibited OC cell proliferation in vitro. Our results demonstrated that PPT2 was considered a favorable prognostic biomarker for OC and may be vital in predicting response to immunotherapy and chemotherapy. Further research was needed to fully understand the relationship between PPT2 and immunotherapy efficacy in OC patients.

Knowledge mapping and visualization of trends in immunotherapy for ovarian cancer over the past five years: a bibliometric analysis.

This study conducts a bibliometric literature analysis to explore trends in immunotherapy for ovarian cancer from 2019 to 2023. An extensive online literature search was conducted in the Web of Science Core Collection database to identify English-language articles and reviews related to "trends in immunotherapy", and "ovarian cancer". statistical analysis was performed using VOSviewer to visualize and compare nations, institutions, and journals simultaneously. Our findings highlight contributions by 118 nations, led by the People's Republic of China with 3,167 contributions; Germany followed with 558 and Italy having 547. Of all publications made between 2019-2023, "Frontiers Immunology" had the most publications with 546 total records followed by "cancers ", and "frontiers in oncology" being the most heavily relied upon categories. Annually publication trends increased until 2022 but then declined considerably as a peak of highly-cited papers occurring between 2019 and 2022. Our bibliometric analysis not only maps the evolution of immunotherapy research in ovarian cancer but also provides actionable insights for advancing scientific progress. By identifying emerging trends and key areas, future research can strategically enhance treatment strategies and outcomes for ovarian cancer patients.

Platinum-based chemotherapy promotes antigen presenting potential in monocytes of patients with high-grade serous ovarian carcinoma.

Ovarian cancer (OC) is the most lethal gynecologic malignancy worldwide. The major clinical challenge includes the asymptomatic state of the disease, making diagnosis possible only at advanced stages. Another OC complication is the high relapse rate and poor prognosis following the standard first-line treatment with platinum-based chemotherapy. At present, numerous clinical trials are being conducted focusing on immunotherapy in OC; nevertheless, there are still no FDA-approved indications. Personalized decision regarding the immunotherapy, including immune checkpoint blockade and immune cell-based immunotherapies, can depend on the effective antigen presentation required for the cytotoxic immune response. The major aim of our study was to uncover tumor-specific transcriptional and epigenetic changes in peripheral blood monocytes in patients with high-grade serous ovarian cancer (HGSOC). Another key point was to elucidate how chemotherapy can reprogram monocytes and how that relates to changes in other immune subpopulations in the blood. To this end, we performed single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from patients with HGSOC who underwent neoadjuvant chemotherapeutic treatment (NACT) and in treatment-naïve patients. Monocyte cluster was significantly affected by tumor-derived factors as well as by chemotherapeutic treatment. Bioinformatical analysis revealed three distinct monocyte subpopulations within PBMCs based on feature gene expression - CD14.Mn.S100A8.9hi, CD14.Mn.MHC2hi and CD16.Mn subsets. The intriguing result was that NACT induced antigen presentation in monocytes by the transcriptional upregulation of MHC class II molecules, but not by epigenetic changes. Increased MHC class II gene expression was a feature observed across all three monocyte subpopulations after chemotherapy. Our data also demonstrated that chemotherapy inhibited interferon-dependent signaling pathways, but activated some TGFb-related genes. Our results can enable personalized decision regarding the necessity to systemically re-educate immune cells to prime ovarian cancer to respond to anti-cancer therapy or to improve personalized prescription of existing immunotherapy in either combination with chemotherapy or a monotherapy regimen.

Niraparib plays synergistic antitumor effects with NRT in a mouse ovarian cancer model with HRP

Niraparib plays synergistic antitumor effects with NRT in a mouse ovarian cancer model with HRP