Single cell sequencing reveals heterogeneity within ovarian cancer epithelium and cancer associated stromal cells

CYP24A1 affected macrophage polarization through degradation of vitamin D as a candidate biomarker for ovarian cancer prognosis

Ovarian cancer (OC) is a fatal gynecological malignancy with a poor prognosis in which mitochondria-related genes are involved deeply. In this study, we aim to screen mitochondria-related genes that play a role in OC prognosis and investigate its effects. Through single-cell sequencing technology and bioinformatics analysis, including TCGA ovarian cancer data analysis, gene expression signature analysis (GES), immune infiltration analysis, Gene Ontology (GO) enrichment analysis, Gene Set Enrichment Analysis (GSEA), and Principal Component Analysis (PCA), our findings revealed that CYP24A1 regulated macrophage polarization through vitamin D (VD) degradation and served as a target gene for the second malignant subtype of OC through bioinformatics analyses. For further validation, the expression and function of CYP24A1 in OC cells was investigated. And the expression of CYP24A1 was much higher in carcinoma than in paracancerous tissue, whereas the VD content decreased in the OC cell lines with CYP24A1 overexpression. Moreover, macrophages were polarized towards M1 after the intervention of VD-treated OC cell lines and inhibited the malignant phenotypes of OC. However, the effect could be reversed by overexpressing CYP24A1, resulting in the polarization of M2 macrophages, thereby promoting tumor progression, as verified by constructing xenograft models in vitro. In conclusion, our findings suggested that CYP24A1 induced M2 macrophage polarization through interaction with VD, thus promoting the malignant progression of OC.

The unveiled mosaic of intra-tumor heterogeneity in ovarian cancer through spatial transcriptomic technologies: A systematic review

Epithelial ovarian cancer is a significant global health issue among women. Diagnosis and treatment pose challenges due to difficulties in predicting patient responses to therapy, primarily stemming from gaps in understanding tumor chemoresistance mechanisms. Recent advancements in transcriptomic technologies like single-cell RNA sequencing and spatial transcriptomics have greatly improved our understanding of ovarian cancer intratumor heterogeneity and tumor microenvironment composition. Spatial transcriptomics, in particular, comprises a plethora of technologies that enable the detection of hundreds of transcriptomes and their spatial distribution within a histological section, facilitating the study of cell types, states, and interactions within the tumor and its microenvironment. Studies investigating the spatial distribution of gene expression in ovarian cancer masses have identified specific features that impact prognosis and therapy outcomes. Emerging evidence suggests that specific spatial patterns of tumor cells and their immune and non-immune microenvironment significantly influence therapy response, as well as the behavior and progression of primary tumors and metastatic sites. The importance of spatially contextualizing ovarian cancer transcriptomes is underscored by these findings, which will advance our understanding and therapeutic approaches for this complex disease.

Single-cell technologies in psoriasis

Psoriasis is a chronic and recurrent inflammatory skin disorder. The primary manifestation of psoriasis arises from disturbances in the cutaneous immune microenvironment, but the specific functions of the cellular components within this microenvironment remain unknown. Recent advancements in single-cell technologies have enabled the detection of multi-omics at the level of individual cells, including single-cell transcriptome, proteome, and metabolome, which have been successfully applied in studying autoimmune diseases, and other pathologies. These techniques allow the identification of heterogeneous cell clusters and their varying contributions to disease development. Considering the immunological traits of psoriasis, an in-depth exploration of immune cells and their interactions with cutaneous parenchymal cells can markedly advance our comprehension of the mechanisms underlying the onset and recurrence of psoriasis. In this comprehensive review, we present an overview of recent applications of single-cell technologies in psoriasis, aiming to improve our understanding of the underlying mechanisms of this disorder.

Unlocking ovarian cancer heterogeneity: advancing immunotherapy through single-cell transcriptomics

Ovarian cancer, a highly fatal gynecological cancer, warrants the need for understanding its heterogeneity. The disease's prevalence and impact are underscored with statistics on mortality rates. Ovarian cancer is categorized into distinct morphological groups, each with its characteristics and prognosis. Despite standard treatments, survival rates remain low due to relapses and chemoresistance. Immune system involvement is evident in ovarian cancer's progression, although the tumor employs immune evasion mechanisms. Immunotherapy, particularly immune checkpoint blockade therapy, is promising, but ovarian cancer's heterogeneity limits its efficacy. Single-cell sequencing technology could be explored as a solution to dissect the heterogeneity within tumor-associated immune cell populations and tumor microenvironments. This cutting-edge technology has the potential to enhance diagnosis, prognosis, and personalized immunotherapy in ovarian cancer, reflecting its broader application in cancer research. The present review focuses on recent advancements and the challenges in applying single-cell transcriptomics to ovarian cancer.

Spatiotemporal architecture of immune cells and cancer-associated fibroblasts in high-grade serous ovarian carcinoma

High-grade serous ovarian carcinoma (HGSOC), the deadliest form of ovarian cancer, is typically diagnosed after it has metastasized and often relapses after standard-of-care platinum-based chemotherapy, likely due to advanced tumor stage, heterogeneity, and immune evasion and tumor-promoting signaling from the tumor microenvironment. To understand how spatial heterogeneity contributes to HGSOC progression and early relapse, we profiled an HGSOC tissue microarray of patient-matched longitudinal samples from 42 patients. We found spatial patterns associated with early relapse, including changes in T cell localization, malformed tertiary lymphoid structure (TLS)-like aggregates, and increased podoplanin-positive cancer-associated fibroblasts (CAFs). Using spatial features to compartmentalize the tissue, we found that plasma cells distribute in two different compartments associated with TLS-like aggregates and CAFs, and these distinct microenvironments may account for the conflicting reports about the role of plasma cells in HGSOC prognosis.

From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy

Despite the tremendous clinical successes recorded in the landscape of cancer therapy, tumor heterogeneity remains a formidable challenge to successful cancer treatment. In recent years, the emergence of high-throughput technologies has advanced our understanding of the variables influencing tumor heterogeneity beyond intrinsic tumor characteristics. Emerging knowledge shows that drivers of tumor heterogeneity are not only intrinsic to cancer cells but can also emanate from their microenvironment, which significantly favors tumor progression and impairs therapeutic response. Although much has been explored to understand the fundamentals of the influence of innate tumor factors on cancer diversity, the roles of the tumor microenvironment (TME) are often undervalued. It is therefore imperative that a clear understanding of the interactions between the TME and other tumor intrinsic factors underlying the plastic molecular behaviors of cancers be identified to develop patient-specific treatment strategies. This review highlights the roles of the TME as an emerging factor in tumor heterogeneity. More particularly, we discuss the role of the TME in the context of tumor heterogeneity and explore the cutting-edge diagnostic and therapeutic approaches that could be used to resolve this recurring clinical conundrum. We conclude by speculating on exciting research questions that can advance our understanding of tumor heterogeneity with the goal of developing customized therapeutic solutions.

A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data

In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).

Single-cell transcriptomic atlas of goat ovarian aging

BACKGROUND

The ovaries are one of the first organs that undergo degenerative changes earlier in the aging process, and ovarian aging is shown by a decrease in the number and quality of oocytes. However, little is known about the molecular mechanisms of female age-related fertility decline in different types of ovarian cells during aging, especially in goats. Therefore, the aim of this study was to reveal the mechanisms driving ovarian aging in goats at single-cell resolution.

RESULTS

For the first time, we surveyed the single-cell transcriptomic landscape of over 27,000 ovarian cells from newborn, young and aging goats, and identified nine ovarian cell types with distinct gene-expression signatures. Functional enrichment analysis showed that ovarian cell types were involved in their own unique biological processes, such as Wnt beta-catenin signalling was enriched in germ cells, whereas ovarian steroidogenesis was enriched in granulosa cells (GCs). Further analysis showed that ovarian aging was linked to GCs-specific changes in the antioxidant system, oxidative phosphorylation, and apoptosis. Subsequently, we identified a series of dynamic genes, such as AMH, CRABP2, THBS1 and TIMP1, which determined the fate of GCs. Additionally, FOXO1, SOX4, and HIF1A were identified as significant regulons that instructed the differentiation of GCs in a distinct manner during ovarian aging.

CONCLUSIONS

This study revealed a comprehensive aging-associated transcriptomic atlas characterizing the cell type-specific mechanisms during ovarian aging at the single-cell level and offers new diagnostic biomarkers and potential therapeutic targets for age-related goat ovarian diseases.

Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.

Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment: A Pilot Study

The prognosis of high-grade serous ovarian carcinoma (HGSOC) is poor, and treatment selection is challenging. A heterogeneous tumor microenvironment (TME) characterizes HGSOC and influences tumor growth, progression, and therapy response. Better characterization with multidimensional approaches for simultaneous identification and categorization of the various cell populations is needed to map the TME complexity. While mass cytometry allows the simultaneous detection of around 40 proteins, the CyTOFmerge MATLAB algorithm integrates data sets and extends the phenotyping. This pilot study explored the potential of combining two datasets for improved TME phenotyping by profiling single-cell suspensions from ten chemo-naïve HGSOC tumors by mass cytometry. A 35-marker pan-tumor dataset and a 34-marker pan-immune dataset were analyzed separately and combined with the CyTOFmerge, merging 18 shared markers. While the merged analysis confirmed heterogeneity across patients, it also identified a main tumor cell subset, additionally to the nine identified by the pan-tumor panel. Furthermore, the expression of traditional immune cell markers on tumor and stromal cells was revealed, as were marker combinations that have rarely been examined on individual cells. This study demonstrates the potential of merging mass cytometry data to generate new hypotheses on tumor biology and predictive biomarker research in HGSOC that could improve treatment effectiveness.

Spatial Heterogeneity in Cytoskeletal Mechanics Response to TGF-β1 and Hypoxia Mediates Partial Epithelial-to-Meshenchymal Transition in Epithelial Ovarian Cancer Cells

Metastatic progression of epithelial ovarian cancer (EOC) involves the partial epithelial-to-mesenchymal transition (EMT) of cancer cells in the primary tumor and dissemination into peritoneal fluid. In part to the high degree of heterogeneity in EOC cells, the identification of EMT in highly epithelial cells in response to differences in matrix mechanics, growth factor signaling, and tissue hypoxia is very difficult. We analyzed different degrees of EMT by tracking changes in cell and nuclear morphology, along with the organization of cytoskeletal proteins. In our analysis, we see a small percentage of individual cells that show dramatic response to TGF-β1 and hypoxia treatment. We demonstrate that EOC cells are spatially aware of their surroundings, with a subpopulation of EOC cells at the periphery of a cell cluster in 2D environments exhibited a greater degree of EMT. These peripheral cancer cells underwent partial EMT, displaying a hybrid of mesenchymal and epithelial characteristics, which often included less cortical actin and more perinuclear cytokeratin expression. Collectively, these data show that tumor-promoting microenvironment conditions can mediate invasive cell behavior in a spatially regulated context in a small subpopulation of highly epithelial clustered cancer cells that maintain epithelial characteristics while also acquiring some mesenchymal traits through partial EMT.

Spatial Transcriptomics Depict Ligand-Receptor Cross-talk Heterogeneity at the Tumor-Stroma Interface in Long-Term Ovarian Cancer Survivors

Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The elucidation of predictive markers of these long-term survivors (LTS) could help identify therapeutic targets for the disease, and thus improve patient survival rates. To investigate the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer, we used spatial transcriptomics to generate spatially resolved transcript profiles in treatment-naïve advanced HGSC from LTS and short-term survivors (STS) and determined the association between cancer-associated fibroblasts (CAF) heterogeneity and survival in patients with advanced HGSC. Spatial transcriptomics and single-cell RNA-sequencing data were integrated to distinguish tumor and stroma regions, and a computational method was developed to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. A specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlated with long-term survival in patients with advanced HGSC. Also, increased APOE-LRP5 cross-talk occurred at the stroma-tumor interface in tumor tissues from STS compared with LTS. These findings were validated using multiplex IHC. Overall, this spatial transcriptomics analysis revealed spatially resolved CAF-tumor cross-talk signaling networks in the ovarian TME that are associated with long-term survival of patients with HGSC. Further studies to confirm whether such cross-talk plays a role in modulating the malignant phenotype of HGSC and could serve as a predictive biomarker of patient survival are warranted.

SIGNIFICANCE

Generation of spatially resolved gene expression patterns in tumors from patients with ovarian cancer surviving more than 10 years allows the identification of novel predictive biomarkers and therapeutic targets for better patient management. See related commentary by Kelliher and Lengyel, p. 1383.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

Multiparameter single-cell proteomic technologies give new insights into the biology of ovarian tumors

High-grade serous ovarian cancer (HGSOC) is the most lethal gynecological malignancy. Its diagnosis at advanced stage compounded with its excessive genomic and cellular heterogeneity make curative treatment challenging. Two critical therapeutic challenges to overcome are carboplatin resistance and lack of response to immunotherapy. Carboplatin resistance results from diverse cell autonomous mechanisms which operate in different combinations within and across tumors. The lack of response to immunotherapy is highly likely to be related to an immunosuppressive HGSOC tumor microenvironment which overrides any clinical benefit. Results from a number of studies, mainly using transcriptomics, indicate that the immune tumor microenvironment (iTME) plays a role in carboplatin response. However, in patients receiving treatment, the exact mechanistic details are unclear. During the past decade, multiplex single-cell proteomic technologies have come to the forefront of biomedical research. Mass cytometry or cytometry by time-of-flight, measures up to 60 parameters in single cells that are in suspension. Multiplex cellular imaging technologies allow simultaneous measurement of up to 60 proteins in single cells with spatial resolution and interrogation of cell-cell interactions. This review suggests that functional interplay between cell autonomous responses to carboplatin and the HGSOC immune tumor microenvironment could be clarified through the application of multiplex single-cell proteomic technologies. We conclude that for better clinical care, multiplex single-cell proteomic technologies could be an integral component of multimodal biomarker development that also includes genomics and radiomics. Collection of matched samples from patients before and on treatment will be critical to the success of these efforts.

Comparative analysis between high-grade serous ovarian cancer and healthy ovarian tissues using single-cell RNA sequencing

Introduction

High-grade serous ovarian cancer (HGSOC) is the most common histological subtype of ovarian cancer, and is associated with high mortality rates.

Methods

In this study, we analyzed specific cell subpopulations and compared different gene functions between healthy ovarian and ovarian cancer cells using single-cell RNA sequencing (ScRNA-seq). We delved deeper into the differences between healthy ovarian and ovarian cancer cells at different levels, and performed specific analysis on endothelial cells.

Results

We obtained scRNA-seq data of 6867 and 17056 cells from healthy ovarian samples and ovarian cancer samples, respectively. The transcriptional profiles of the groups differed at various stages of ovarian cell development. A detailed comparison of the cell cycle, and cell communication of different groups, revealed significant differences between healthy ovarian and ovarian cancer cells. We also found that apoptosis-related genes, URI1, PAK2, PARP1, CLU and TIMP3, were highly expressed, while immune-related genes, UBB, RPL11, CAV1, NUPR1 and Hsp90ab1, were lowly expressed in ovarian cancer cells. The results of the ScRNA-seq were verified using qPCR.

Discussion

Our findings revealed differences in function, gene expression and cell interaction patterns between ovarian cancer and healthy ovarian cell populations. These findings provide key insights on further research into the treatment of ovarian cancer.

Application of Single-Cell RNA Sequencing in Ovarian Development

The ovary is a female reproductive organ that plays a key role in fertility and the maintenance of endocrine homeostasis, which is of great importance to women's health. It is characterized by a high heterogeneity, with different cellular subpopulations primarily containing oocytes, granulosa cells, stromal cells, endothelial cells, vascular smooth muscle cells, and diverse immune cell types. Each has unique and important functions. From the fetal period to old age, the ovary experiences continuous structural and functional changes, with the gene expression of each cell type undergoing dramatic changes. In addition, ovarian development strongly relies on the communication between germ and somatic cells. Compared to traditional bulk RNA sequencing techniques, the single-cell RNA sequencing (scRNA-seq) approach has substantial advantages in analyzing individual cells within an ever-changing and complicated tissue, classifying them into cell types, characterizing single cells, delineating the cellular developmental trajectory, and studying cell-to-cell interactions. In this review, we present single-cell transcriptome mapping of the ovary, summarize the characteristics of the important constituent cells of the ovary and the critical cellular developmental processes, and describe key signaling pathways for cell-to-cell communication in the ovary, as revealed by scRNA-seq. This review will undoubtedly improve our understanding of the characteristics of ovarian cells and development, thus enabling the identification of novel therapeutic targets for ovarian-related diseases.

Comprehensive bioinformatics analysis confirms RBMS3 as the central candidate biological target for ovarian cancer

Ovarian cancer (OC) is one of the most lethal malignancies in the female reproductive system. To find genes related to cancer progression targeting specific biological factors for targeted therapy, bioinformatics technology has been widely used. To screen the prognostic gene markers of OC by bioinformatics and explore their potential molecular biological mechanisms. Two data sets related to OC, GSE54388, and GSE119056, were rooted in the open comprehensive gene expression database (GEO). To correct the background of the data, standardize and screen differentially expressed genes (DEGs) using the R software limma package. The selected DEGs were enriched by Gene Ontology (GO) and through DAVID online database. Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis and protein-protein interaction network (PPI-network) map were constructed by STRING online database and Cytoscape software. Combined with the TCGA database, univariate and multivariate COX regression were used to screen prognostic genes. QRT-PCR was used to verify DEGs in clinical tissue samples. Eventually, the function of RBMS3 on the viability, migration, invasion, and apoptosis of OC cells was tested through functional experiments in vitro. 352 common DEGs were screened from GSE54388 and GSE119056 data sets. Survival analysis showed that MEIS2, TSTA3, CNTN1, RBMS3, and TRA2A were considered to be connected with the prognosis of OC. We discover that the expression level of RBMS3 was positively connected with the overall survival (OS) rate of sufferers with OC. The level of RBMS3 in OC tissues was markedly lower than that in neighboring structures and the outcomes of the GEPIA database were consistent with those of the qRT-PCR experiment. Through gene transfection technology it was found that overexpression of RBMS3 in OC cells substantially suppressed the vitality, migration, and invasion of OC cells and raised the rates of apoptosis in the OC cells. In this experiment, we distinguish 5 genes that may participate in the prognosis of OC and showed the key genes and pathways related to OC. It is speculated that RBMS3, a tumor suppressor gene, can be applied as a potential biological marker for the treatment of OC, gene expression summary, and prognosis.

Forward Genetic Screens as Tools to Investigate Role and Mechanisms of EMT in Cancer

Epithelial-mesenchymal transition (EMT) is a process of cellular plasticity regulated by complex signaling networks. Under physiological conditions, it plays an important role in wound healing and organ repair. Its importance for human disease is given by its central role in chronic fibroproliferative diseases and cancer, which represent leading causes of death worldwide. In tumors, EMT is involved in primary tumor growth, metastasis and therapy resistance. It is therefore a major requisite to investigate and understand the role of EMT and the mechanisms leading to EMT in order to tackle these diseases therapeutically. Forward genetic screens link genome modifications to phenotypes, and have been successfully employed to identify oncogenes, tumor suppressor genes and genes involved in metastasis or therapy resistance. In particular, transposon-based insertional mutagenesis screens and CRISPR-based screens are versatile and easy-to-use tools applied in recent years to discover and identify novel cancer-related mechanisms. Here, we review the contribution of forward genetic screens to our understanding of how EMT is regulated and how it is involved in various aspects of cancer. Based on the current literature, we propose these methods as additional tools to investigate EMT.

Establishment of an ovarian cancer omentum metastasis-related prognostic model by integrated analysis of scRNA-seq and bulk RNA-seq

OBJECTIVE

Ovarian cancer has the highest mortality rate among gynecological malignant tumors, and it preferentially metastasizes to omental tissue, leading to intestinal obstruction and death. scRNA-seq is a powerful technique to reveal tumor heterogeneity. Analyzing omentum metastasis of ovarian cancer at the single-cell level may be more conducive to exploring and understanding omentum metastasis and prognosis of ovarian cancer at the cellular function and genetic levels.

METHODS

The omentum metastasis site scRNA-seq data of GSE147082 were acquired from the GEO (Gene Expression Omnibus) database, and single cells were clustered by the Seruat package and annotated by the SingleR package. Cell differentiation trajectories were reconstructed through the monocle package. The ovarian cancer microarray data of GSE132342 were downloaded from GEO and were clustered by using the ConsensusClusterPlus package into omentum metastasis-associated clusters according to the marker genes gained from single-cell differentiation trajectory analysis. The tumor microenvironment (TME) and immune infiltration differences between clusters were analyzed by the estimate and CIBERSORT packages. The expression matrix of genes used to cluster GSE132342 patients was extracted from bulk RNA-seq data of TCGA-OV (The Cancer Genome Atlas ovarian cancer), and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to establish an omentum metastasis-associated gene (OMAG) signature. The signature was then tested by GSE132342 data. Finally, the clinicopathological characteristics of TCGA-OV were screened by univariate and multivariate Cox regression analysis to draw the nomogram.

RESULTS

A total of 9885 cells from 6 patients were clustered into 18 cell clusters and annotated into 14 cell types. Reconstruction of differentiation trajectories divided the cells into 5 branches, and a total of 781 cell trajectory-related characteristic genes were obtained. A total of 3769 patients in GSE132342 were subtyped into 3 clusters by 74 cell trajectory-related characteristic genes. Kaplan-Meier (K-M) survival analysis showed that the prognosis of cluster 2 was the worst, P < 0.001. The TME analysis showed that the ESTIMATE score and stromal score in cluster 2 were significantly higher than those in the other two clusters, P < 0.001. The immune infiltration analysis showed differences in the fraction of 8 immune cells among the 3 clusters, P < 0.05. The expression data of 74 genes used for GEO clustering were extracted from 379 patients in TCGA-OV, and combined with survival information, 10 candidates for OMAGs were filtered by LASSO. By using multivariate Cox regression, the 6-OMAGs signature was established as RiskScore = 0.307*TIMP3 + 3.516*FBN1-0.109*IGKC + 0.209*RPL21 + 0.870*UCHL1 + 0.365*RARRES1. Taking TCGA-OV as the training set and GSE132342 as the test set, receiver operating characteristic (ROC) curves were drawn to verify the prognostic value of 6-OMAGs. Screened by univariate and multivariate Cox regression analysis, 3 (age, cancer status, primary therapy outcome) of 5 clinicopathological characteristics were used to construct the nomogram combined with risk score.

CONCLUSION

We constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis.

Single-cell and bulk RNA sequencing reveal ligands and receptors associated with worse overall survival in serous ovarian cancer

Background

Serous ovarian carcinoma is the most frequent histological subgroup of ovarian cancer and the leading cause of death among gynecologic tumors. The tumor microenvironment and cancer-associated fibroblasts (CAFs) have a critical role in the origin and progression of cancer. We comprehensively characterized the crosstalk between CAFs and ovarian cancer cells from malignant fluids to identify specific ligands and receptors mediating intercellular communications and disrupted pathways related to prognosis and therapy response.

Methods

Malignant fluids of serous ovarian cancer, including tumor-derived organoids, CAFs-enriched (eCAFs), and malignant effusion cells (no cultured) paired with normal ovarian tissues, were explored by RNA-sequencing. These data were integrated with single-cell RNA-sequencing data of ascites from ovarian cancer patients. The most relevant ligand and receptor interactions were used to identify differentially expressed genes with prognostic values in ovarian cancer.

Results

CAF ligands and epithelial cancer cell receptors were enriched for PI3K-AKT, focal adhesion, and epithelial-mesenchymal transition signaling pathways. Collagens, MIF, MDK, APP, and laminin were detected as the most significant signaling, and the top ligand-receptor interactions THBS2/THBS3 (CAFs)—CD47 (cancer cells), MDK (CAFs)—NCL/SDC2/SDC4 (cancer cells) as potential therapeutic targets. Interestingly, 34 genes encoding receptors and ligands of the PI3K pathway were associated with the outcome, response to treatment, and overall survival in ovarian cancer. Up-regulated genes from this list consistently predicted a worse overall survival (hazard ratio > 1.0 and log-rank P < 0.05) in two independent validation cohorts.

Conclusions

This study describes critical signaling pathways, ligands, and receptors involved in the communication between CAFs and cancer cells that have prognostic and therapeutic significance in ovarian cancer.

Single-Cell Dissection of the Multiomic Landscape of High-Grade Serous Ovarian Cancer

High-grade serous cancer (HGSC) is the most common subtype of ovarian cancer. HGSC is highly aggressive with poor patient outcomes, and a deeper understanding of HGSC tumorigenesis could help guide future treatment development. To systematically characterize the underlying pathologic mechanisms and intratumoral heterogeneity in human HGSC, we used an optimized single-cell multiomics sequencing technology to simultaneously analyze somatic copy-number alterations (SCNA), DNA methylation, chromatin accessibility, and transcriptome in individual cancer cells. Genes associated with interferon signaling, metallothioneins, and metabolism were commonly upregulated in ovarian cancer cells. Integrated multiomics analyses revealed that upregulation of interferon signaling and metallothioneins was influenced by both demethylation of their promoters and hypomethylation of satellites and LINE1, and potential key transcription factors regulating glycolysis using chromatin accessibility data were uncovered. In addition, gene expression and DNA methylation displayed similar patterns in matched primary and abdominal metastatic tumor cells of the same genetic lineage, suggesting that metastatic cells potentially preexist in the subclones of primary tumors. Finally, the lineages of cancer cells with higher residual DNA methylation levels and upregulated expression of CCN1 and HSP90AA1 presented greater metastatic potential. This study characterizes the critical genetic, epigenetic, and transcriptomic features and their mutual regulatory relationships in ovarian cancer, providing valuable resources for identifying new molecular mechanisms and potential therapeutic targets for HGSC.

SIGNIFICANCE

Integrated analysis of multiomic changes and epigenetic regulation in high-grade serous ovarian cancer provides insights into the molecular characteristics of this disease, which could help improve diagnosis and treatment.

Single-Cell RNA-Sequencing Atlas Reveals the Tumor Microenvironment of Metastatic High-Grade Serous Ovarian Carcinoma

Ovarian cancer is the most common and lethal gynecological tumor in women worldwide. High-grade serous ovarian carcinoma (HGSOC) is one of the histological subtypes of epithelial ovarian cancer, accounting for 70%. It often occurs at later stages associated with a more fatal prognosis than endometrioid carcinomas (EC), another subtype of epithelial ovarian cancer. However, the molecular mechanism and biology underlying the metastatic HGSOC (HG_M) immunophenotype remain poorly elusive. Here, we performed single-cell RNA sequencing analyses of primary HGSOC (HG_P) samples, metastatic HGSOC (HG_M) samples, and endometrioid carcinomas (EC) samples. We found that ERBB2 and HOXB-AS3 genes were more amplified in metastasis tumors than in primary tumors. Notably, high-grade serous ovarian cancer metastases are accompanied by dysregulation of multiple pathways. Malignant cells with features of epithelial-mesenchymal transition (EMT) affiliated with poor overall survival were identified. In addition, cancer-associated fibroblasts with EMT-program were enriched in HG_M, participating in angiogenesis and immune regulation, such as IL6/STAT3 pathway activity. Compared with ECs, HGSOCs exhibited higher T cell infiltration. PRDM1 regulators may be involved in T cell exhaustion in ovarian cancer. The CX3CR1_macro subpopulation may play a role in promoting tumor progression in ovarian cancer with high expression of BAG3, IL1B, and VEGFA. The new targets we discovered in this study will be useful in the future, providing guidance on the treatment of ovarian cancer.

A bioinformatic analysis of WFDC2 (HE4) expression in high grade serous ovarian cancer reveals tumor-specific changes in metabolic and extracellular matrix gene expression

Human epididymis protein-4 (HE4/WFDC2) has been well-studied as an ovarian cancer clinical biomarker. To improve our understanding of its functional role in high grade serous ovarian cancer, we determined transcriptomic differences between ovarian tumors with high- versus low-WFDC2 mRNA levels in The Cancer Genome Atlas dataset. High-WFDC2 transcript levels were significantly associated with reduced survival in stage III/IV serous ovarian cancer patients. Differential expression and correlation analyses revealed secretory leukocyte peptidase inhibitor (SLPI/WFDC4) as the gene most positively correlated with WFDC2, while A kinase anchor protein-12 was most negatively correlated. WFDC2 and SLPI were strongly correlated across many cancers. Gene ontology analysis revealed enrichment of oxidative phosphorylation in differentially expressed genes associated with high-WFDC2 levels, while extracellular matrix organization was enriched among genes associated with low-WFDC2 levels. Immune cell subsets found to be positively correlated with WFDC2 levels were B cells and plasmacytoid dendritic cells, while neutrophils and endothelial cells were negatively correlated with WFDC2. Results were compared with DepMap cell culture gene expression data. Gene ontology analysis of k-means clustering revealed that genes associated with low-WFDC2 were also enriched in extracellular matrix and adhesion categories, while high-WFDC2 genes were enriched in epithelial cell proliferation and peptidase activity. These results support previous findings regarding the effect of HE4/WFDC2 on ovarian cancer pathogenesis in cell lines and mouse models, while adding another layer of complexity to its potential functions in ovarian tumor tissue. Further experimental explorations of these findings in the context of the tumor microenvironment are merited.

Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis

Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.

Single-Cell Sequencing Technologies in Precision Oncology

Single-cell sequencing technologies are revolutionizing cancer research and are poised to become the standard for translational cancer studies. Rapidly decreasing costs and increasing throughput and resolution are paving the way for the adoption of single-cell technologies in clinical settings for personalized medicine applications. In this chapter, we review the state of the art of single-cell DNA and RNA sequencing technologies, the computational tools to analyze the data, and their potential application to precision oncology. We also discuss the advantages of single-cell over bulk sequencing for the dissection of intra-tumor heterogeneity and the characterization of subclonal cell populations, the implementation of targeted drug repurposing approaches, and describe advanced methodologies for multi-omics data integration and to assess cell signaling at single-cell resolution.

Multiparameter Single-Cell Characterization of Ovarian Intratumor Heterogeneity

Cancer is a complex disease rooted in heterogeneity, which is the phenomenon of individual cells, tissues, or patients having distinct phenotypic and/or genetic characteristics. Observed divergent disease etiology is likely rooted, at least in part, in tumor heterogeneity and the classification of distinct and important subpopulations of cells within the tumor and its associated microenvironment has remained a technical challenge. Standard next-generation sequencing of bulk tumor tissue provides an overall average genetic profile of the sample, and masks contributions from individual cells and minor populations of cells, particularly in heterogeneous samples. Only with the advent of single-cell analysis and sequencing technologies has it become possible to characterize key contributions of cellular subpopulations in order to more comprehensively characterize disease. This chapter describes a method to generate linked phenotypic and genotypic data at single-cell resolution using a real-time single-cell resolved platform. Specifically, the example method provided here is used to link cellular growth kinetics and expression of a prognostic marker protein, CA-125, in cells derived from ovarian cancer patients with their single-cell genomic profiles, but the method is translatable to other cell types and phenotypes of interest.

The proteomic landscape of ovarian cancer cells in response to melatonin

Ovarian cancer (OC) is the most lethal gynecological malignancy with a highly negative prognosis. Melatonin is an indoleamine secreted by the pineal gland during darkness and has shown antitumor activity in both in vitro and in vivo experiments. Herein, we investigated the influence of melatonin on the proteome of human ovarian carcinoma cells (SKOV-3 cell line) using the Ultimate 3000 LC Liquid NanoChromatography equipment coupled to a Q-Exactive mass spectrometry. After 48 h of treatment, melatonin induced a significant cytotoxicity especially with the highest melatonin concentration. The proteomic profile revealed 639 proteins in the control group, and 98, 110, and 128 proteins were altered by melatonin at the doses of 0.8, 1.6, and 2.4 mM, respectively. Proteins associated with the immune system and tricarboxylic acid cycle were increased in the three melatonin-exposed groups of cells. Specifically, the dose of 2.4 mM led to a reduction in molecules associated with protein synthesis, especially those of the ribosomal protein family. We also identified 28 potential genes shared between normal ovarian tissue and OC in all experimental groups, and melatonin was predicted to alter genes encoding ribosomal proteins. Notably, the set of proteins changed by melatonin was linked to a better prognosis for OC patients. We conclude that melatonin significantly alters the proteome of SKOV-3 cells by changing proteins involved with the immune response and mitochondrial metabolism. The concentration of 2.4 mM of melatonin promoted the largest number of protein changes. The evidence suggests that melatonin may be an effective therapeutic strategy against OC.

Single-Cell RNA-Seq Technologies and Computational Analysis Tools: Application in Cancer Research

The recent maturation of single-cell RNA sequencing (scRNA-seq) provides unique opportunities for researchers to uncover new and potentially unexpected biological discoveries and to understand the complexity of tissues by transcriptomic profiling in individual cells. This review introduces the latest scRNA-seq techniques and platforms as well as their advantages and disadvantages. Moreover, we review computational tools and pipelines for analyzing scRNA-seq data, and their applications in cancer research, highlighting the important role of scRNA-seq techniques in this area.

Integration of Genomic Profiling and Organoid Development in Precision Oncology

Precision oncology involves an innovative personalized treatment strategy for each cancer patient that provides strategies and options for cancer treatment. Currently, personalized cancer medicine is primarily based on molecular matching. Next-generation sequencing and related technologies, such as single-cell whole-transcriptome sequencing, enable the accurate elucidation of the genetic landscape in individual cancer patients and consequently provide clinical benefits. Furthermore, advances in cancer organoid models that represent genetic variations and mutations in individual cancer patients have direct and important clinical implications in precision oncology. This review aimed to discuss recent advances, clinical potential, and limitations of genomic profiling and the use of organoids in breast and ovarian cancer. We also discuss the integration of genomic profiling and organoid models for applications in cancer precision medicine.

Single-Cell Transcriptome Analysis Reveals RGS1 as a New Marker and Promoting Factor for T-Cell Exhaustion in Multiple Cancers

T-cell exhaustion is one of the main reasons of tumor immune escape. Using single-cell transcriptome data of CD8+ T cells in multiple cancers, we identified different cell types, in which Pre_exhaust and exhausted T cells participated in negative regulation of immune system process. By analyzing the coexpression network patterns and differentially expressed genes of Pre_exhaust, exhausted, and effector T cells, we identified 35 genes related to T-cell exhaustion, whose high GSVA scores were associated with significantly poor prognosis in various cancers. In the differentially expressed genes, RGS1 showed the greatest fold change in Pre_exhaust and exhausted cells of three cancers compared with effector T cells, and high expression of RGS1 was also associated with poor prognosis in various cancers. Additionally, RGS1 protein was upregulated significantly in tumor tissues in the immunohistochemistry verification. Furthermore, RGS1 displayed positive correlation with the 35 genes, especially highly correlated with PDCD1, CTLA4, HAVCR2, and TNFRSF9 in CD8+ T cells and cancer tissues, indicating the important roles of RGS1 in CD8+ T-cell exhaustion. Considering the GTP-hydrolysis activity of RGS1 and significantly high mRNA and protein expression in cancer tissues, we speculated that RGS1 potentially mediate the T-cell retention to lead to the persistent antigen stimulation, resulting in T-cell exhaustion. In conclusion, our findings suggest that RGS1 is a new marker and promoting factor for CD8+ T-cell exhaustion and provide theoretical basis for research and immunotherapy of exhausted cells.

The multicellular signalling network of ovarian cancer metastases

BACKGROUND

Transcoelomic spread is the major route of metastasis of ovarian high-grade serous carcinoma (HGSC) with the omentum as the major metastatic site. Its unique tumour microenvironment with its large populations of adipocytes, mesothelial cells and immune cells establishes an intercellular signaling network that is instrumental for metastatic growth yet poorly understood.

METHODS

Based on transcriptomic analysis of tumour cells, tumour-associated immune and stroma cells we defined intercellular signaling pathways for 284 cytokines and growth factors and their cognate receptors after bioinformatic adjustment for contaminating cell types. The significance of individual components of this network was validated by analysing clinical correlations and potentially pro-metastatic functions, including tumour cell migration, pro-inflammatory signal transduction and TAM expansion.

RESULTS

The data show an unexpected prominent role of host cells, and in particular of omental adipocytes, mesothelial cells and fibroblasts (CAF), in sustaining this signaling network. These cells, rather than tumour cells, are the major source of most cytokines and growth factors in the omental microenvironment (n = 176 vs. n = 13). Many of these factors target tumour cells, are linked to metastasis and are associated with a short survival. Likewise, tumour stroma cells play a major role in extracellular-matrix-triggered signaling. We have verified the functional significance of our observations for three exemplary instances. We show that the omental microenvironment (i) stimulates tumour cell migration and adhesion via WNT4 which is highly expressed by CAF; (ii) induces pro-tumourigenic TAM proliferation in conjunction with high CSF1 expression by omental stroma cells and (iii) triggers pro-inflammatory signaling, at least in part via a HSP70-NF-κB pathway.

CONCLUSIONS

The intercellular signaling network of omental metastases is majorly dependent on factors secreted by immune and stroma cells to provide an environment that supports ovarian HGSC progression. Clinically relevant pathways within this network represent novel options for therapeutic intervention.

Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression

Background

Ovarian cancer was one of the leading causes of female deaths. Patients with OC were essentially incurable and portends a poor prognosis, presumably because of profound genetic heterogeneity limiting reproducible prognostic classifications.

Methods

We comprehensively analyzed an ovarian cancer single-cell RNA sequencing dataset, GSE118828, and identified nine major cell types. Relationship between the clusters was explored with CellPhoneDB. A malignant epithelial cluster was confirmed using pseudotime analysis, CNV and GSVA. Furthermore, we constructed the prediction model (i.e., RiskScore) consisted of 10 prognosis-specific genes from 2397 malignant epithelial genes using the LASSO Cox regression algorithm based on public datasets. Then, the prognostic value of Riskscore was assessed with Kaplan–Meier survival analysis and time-dependent ROC curves. At last, a series of in-vitro assays were conducted to explore the roles of IL4I1, an important gene in Riskscore, in OC progression.

Results

We found that macrophages possessed the most interaction pairs with other clusters, and M2-like TAMs were the dominant type of macrophages. C0 was identified as the malignant epithelial cluster. Patients with a lower RiskScore had a greater OS (log-rank P < 0.01). In training set, the AUC of RiskScore was 0.666, 0.743 and 0.809 in 1-year, 3-year and 5-year survival, respectively. This was also validated in another two cohorts. Moreover, downregulation of IL4I1 inhibited OC cells proliferation, migration and invasion.

Conclusions

Our work provide novel insights into our understanding of the heterogeneity among OCs, and would help elucidate the biology of OC and provide clinical guidance in prognosis for OC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03123-7.

Cancer cell immune mimicry delineates onco-immunologic modulation

Immune transcripts are essential for depicting onco-immunologic interactions. However, whether cancer cells mimic immune transcripts to reprogram onco-immunologic interaction remains unclear. Here, single-cell transcriptomic analyses of 7,737 normal and 37,476 cancer cells reveal increased immune transcripts in cancer cells. Cells gradually acquire immune transcripts in malignant transformation. Notably, cancer cell-derived immune transcripts contribute to distinct prognoses of immune gene signatures. Optimized immune response signature (oIRS), obtained by excluding cancer-related immune genes from immune gene signatures, and offers a more reliable prognostic value. oIRS reveals that antigen presentation, NK cell killing and T cell signaling are associated with favorable prognosis. Patients with higher oIRS expression are associated with favorable responses to immunotherapy. Indeed, CD83⁺ cell infiltration, which indicates antigen presentation activity, predicts favorable prognosis in breast cancer. These findings unveil that immune mimicry is a distinct cancer hallmark, providing an example of cancer cell plasticity and a refined view of tumor microenvironment.

•

Single-cell transcriptome reveals immune mimicry as a distinct cancer hallmark

•

Mimicry transcripts contribute to distinct prognoses of immune gene signatures

•

oIRS refines prognostic evaluation and points to core favorable immune processes

•

oIRS signifies the role of antigen presentation and indicates immunotherapy response

Identification of Specific Cell Subpopulations and Marker Genes in Ovarian Cancer Using Single-Cell RNA Sequencing

Objective

Ovarian cancer is the deadliest gynaecological cancer globally. In our study, we aimed to analyze specific cell subpopulations and marker genes among ovarian cancer cells by single-cell RNA sequencing (RNA-seq).

Methods

Single-cell RNA-seq data of 66 high-grade serous ovarian cancer cells were employed from the Gene Expression Omnibus (GEO). Using the Seurat package, we performed quality control to remove cells with low quality. After normalization, we detected highly variable genes across the single cells. Then, principal component analysis (PCA) and cell clustering were performed. The marker genes in different cell clusters were detected. A total of 568 ovarian cancer samples and 8 normal ovarian samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes were identified according to ∣log2fold change (FC) | >1 and adjusted p value <0.05. To explore potential biological processes and pathways, functional enrichment analyses were performed. Furthermore, survival analyses of differentially expressed marker genes were performed.

Results

After normalization, 6000 highly variable genes were identified across the single cells. The cells were divided into 3 cell populations, including G1, G2M, and S cell cycles. A total of 1,124 differentially expressed genes were identified in ovarian cancer samples. These differentially expressed genes were enriched in several pathways associated with cancer, such as metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway. Furthermore, marker genes, STAT1, ANP32E, GPRC5A, and EGFL6, were highly expressed in ovarian cancer, while PMP22, FBXO21, and CYB5R3 were lowly expressed in ovarian cancer. These marker genes were positively associated with prognosis of ovarian cancer.

Conclusion

Our findings revealed specific cell subpopulations and marker genes in ovarian cancer using single-cell RNA-seq, which provided a novel insight into the heterogeneity of ovarian cancer.

Estrogens and the Schrödinger's Cat in the Ovarian Tumor Microenvironment

Simple Summary

Ovarian cancer is a complex pathology for which we require effective screening and therapeutical strategies. Apart from the cancer cell portion, there exist plastic immune and non-immune cell populations, jointly constituting the context-adaptive tumor microenvironment, which is pivotal in tumorigenesis. Estrogens might be synthesized in the ovarian tumor tissue and actively contribute to the shaping of an immunosuppressive microenvironment. Current immune therapies have limited effectiveness as a multitude of factors influence the outcome. A thorough understanding of the ovarian cancer biology is crucial in the efforts to reestablish homeostasis.

Abstract

Ovarian cancer is a heterogeneous disease affecting the aging ovary, in concert with a complex network of cells and signals, together representing the ovarian tumor microenvironment. As in the “Schrödinger’s cat” thought experiment, the context-dependent constituents of the—by the time of diagnosis—well-established tumor microenvironment may display a tumor-protective and -destructive role. Systemic and locally synthesized estrogens contribute to the formation of a pro-tumoral microenvironment that enables the sustained tumor growth, invasion and metastasis. Here we focus on the estrogen biosynthetic and metabolic pathways in ovarian cancer and elaborate their actions on phenotypically plastic, estrogen-responsive, aging immune cells of the tumor microenvironment, altogether highlighting the multicomponent-connectedness and complexity of cancer, and contributing to a broader understanding of the ovarian cancer biology.

Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges

Ovarian cancer remains the leading cause of death from gynecologic malignancy in the Western world. Tumors are comprised of heterogeneous populations of various cancer, immune, and stromal cells; it is hypothesized that rare cancer stem cells within these subpopulations lead to disease recurrence and treatment resistance. Technological advances now allow for the analysis of tumor genomes and transcriptomes at the single-cell level, which provides the resolution to potentially identify these rare cancer stem cells within the larger tumor.In this chapter, we review the evolution of next-generation RNA sequencing techniques, the methodology of single-cell isolation and sequencing, sequencing data analysis, and the potential applications in ovarian cancer. We also summarize the current published work using single-cell sequencing in ovarian cancer.By utilizing this novel technique to characterize the gene expression of rare subpopulations, new targets and treatment pathways may be identified in ovarian cancer to change treatment paradigms.

Extensive three-dimensional intratumor proteomic heterogeneity revealed by multiregion sampling in high-grade serous ovarian tumor specimens

Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten high-grade serous ovarian carcinomas (HGSOCs) and analyzed by mass spectrometry, reverse phase protein arrays, and RNA sequencing. Unsupervised analyses of protein abundance data revealed independent clustering of an enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering driven by overall tumor "purity." Comparing these data to previously defined prognostic HGSOC molecular subtypes revealed protein and transcript expression from tumor epithelium correlated with the differentiated subtype, whereas stromal proteins (and transcripts) correlated with the mesenchymal subtype. Protein and transcript abundance in the tumor epithelium and stroma exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial tumor microenvironment protein heterogeneity that directly bears on prognostic signatures, biomarker discovery, and cancer pathophysiology and underscore the need to enrich cellular subpopulations for expression profiling.

Shaping Immune Responses in the Tumor Microenvironment of Ovarian Cancer

Reciprocal signaling between immune cells and ovarian cancer cells in the tumor microenvironment can alter immune responses and regulate disease progression. These signaling events are regulated by multiple factors, including genetic and epigenetic alterations in both the ovarian cancer cells and immune cells, as well as cytokine pathways. Multiple immune cell types are recruited to the ovarian cancer tumor microenvironment, and new insights about the complexity of their interactions have emerged in recent years. The growing understanding of immune cell function in the ovarian cancer tumor microenvironment has important implications for biomarker discovery and therapeutic development. This review aims to describe the factors that shape the phenotypes of immune cells in the tumor microenvironment of ovarian cancer and how these changes impact disease progression and therapy.

Cancer Explant Models

Overcoming the challenges of understanding and treating cancer requires reliable patient-derived models of cancer (PDMCs). For decades, cancer research and therapeutic development relied primarily on cancer cell lines because of their prevalence, reproducibility, and simplicity to maintain. However, findings from research conducted in cell lines are rarely recapitulated in vivo and seldom directly translatable to patients. The tumor microenvironment (TME), tumor-stromal interactions, and associations with host immune cells produce profound changes in tumor phenotype and complexity not captured in traditional monolayer cell culture. In this chapter, we present various cancer explant models and discuss their applicability based on specific research aims. We discuss the appropriateness of these models for basic science questions, drug screening/development, and for personalized, precision medicine. We also consider logistical factors such as resource cost, technical difficulty, and accessibility. We finish this chapter with a practical guide intended to help the reader select the cancer explant model system(s) that best address their research aims.

Advances in understanding the molecular pathology of gynecological malignancies: the role and potential of RNA sequencing

For many years technological limitations restricted the progress of identifying the underlying genetic causes of gynecologicalcancers. However, during the past decade, high-throughput next-generation sequencing technologies have revolutionized cancer research. RNA sequencing has arisen as a very useful technique in expanding our understanding of genome changes in cancer. Cancer is characterized by the accumulation of genetic alterations affecting genes, including substitutions, insertions, deletions, translocations, gene fusions, and alternative splicing. If these aberrant genes become transcribed, aberrations can be detected by RNA sequencing, which will also provide information on the transcript abundance revealing the expression levels of the aberrant genes. RNA sequencing is considered the technique of choice when studying gene expression and identifying new RNA species. This is due to the quantitative and qualitative improvement that it has brought to transcriptome analysis, offering a resolution that allows research into different layers of transcriptome complexity. It has also been successful in identifying biomarkers, fusion genes, tumor suppressors, and uncovering new targets responsible for drug resistance in gynecological cancers. To illustrate that we here review the role of RNA sequencing in studies that enhanced our understanding of the molecular pathology of gynecological cancers.

Co-regulation and function of FOXM1/RHNO1 bidirectional genes in cancer

The FOXM1 transcription factor is an oncoprotein and a top biomarker of poor prognosis in human cancer. Overexpression and activation of FOXM1 is frequent in high-grade serous carcinoma (HGSC), the most common and lethal form of human ovarian cancer, and is linked to copy number gains at chromosome 12p13.33. We show that FOXM1 is co-amplified and co-expressed with RHNO1, a gene involved in the ATR-Chk1 signaling pathway that functions in the DNA replication stress response. We demonstrate that FOXM1 and RHNO1 are head-to-head (i.e., bidirectional) genes (BDG) regulated by a bidirectional promoter (BDP) (named F/R-BDP). FOXM1 and RHNO1 each promote oncogenic phenotypes in HGSC cells, including clonogenic growth, DNA homologous recombination repair, and poly-ADP ribosylase inhibitor resistance. FOXM1 and RHNO1 are one of the first examples of oncogenic BDG, and therapeutic targeting of FOXM1/RHNO1 BDG is a potential therapeutic approach for ovarian and other cancers.

Application of single-cell sequencing technologies in pancreatic cancer

Pancreatic cancer (PC) is the third lethal disease for cancer-related mortalities globally. This is mainly because of the aggressive nature and heterogeneity of the disease that is diagnosed only in their advanced stages. Thus, it is challenging for researchers and clinicians to study the molecular mechanism involved in the development of this aggressive disease. The single-cell sequencing technology enables researchers to study each and every individual cell in a single tumor. It can be used to detect genome, transcriptome, and multi-omics of single cells. The current single-cell sequencing technology is now becoming an important tool for the biological analysis of cells, to find evolutionary relationship between multiple cells and unmask the heterogeneity present in the tumor cells. Moreover, its sensitivity nature is found progressive enabling to detect rare cancer cells, circulating tumor cells, metastatic cells, and analyze the intratumor heterogeneity. Furthermore, these single-cell sequencing technologies also promoted personalized treatment strategies and next-generation sequencing to predict the disease. In this review, we have focused on the applications of single-cell sequencing technology in identifying cancer-associated cells like cancer-associated fibroblast via detecting circulating tumor cells. We also included advanced technologies involved in single-cell sequencing and their advantages. The future research indeed brings the single-cell sequencing into the clinical arena and thus could be beneficial for diagnosis and therapy of PC patients.

Stiffness increases with myofibroblast content and collagen density in mesenchymal high grade serous ovarian cancer

Women diagnosed with high-grade serous ovarian cancers (HGSOC) are still likely to exhibit a bad prognosis, particularly when suffering from HGSOC of the Mesenchymal molecular subtype (50% cases). These tumors show a desmoplastic reaction with accumulation of extracellular matrix proteins and high content of cancer-associated fibroblasts. Using patient-derived xenograft mouse models of Mesenchymal and Non-Mesenchymal HGSOC, we show here that HGSOC exhibit distinct stiffness depending on their molecular subtype. Indeed, tumor stiffness strongly correlates with tumor growth in Mesenchymal HGSOC, while Non-Mesenchymal tumors remain soft. Moreover, we observe that tumor stiffening is associated with high stromal content, collagen network remodeling, and MAPK/MEK pathway activation. Furthermore, tumor stiffness accompanies a glycolytic metabolic switch in the epithelial compartment, as expected based on Warburg's effect, but also in stromal cells. This effect is restricted to the central part of stiff Mesenchymal tumors. Indeed, stiff Mesenchymal tumors remain softer at the periphery than at the core, with stromal cells secreting high levels of collagens and showing an OXPHOS metabolism. Thus, our study suggests that tumor stiffness could be at the crossroad of three major processes, i.e. matrix remodeling, MEK activation and stromal metabolic switch that might explain at least in part Mesenchymal HGSOC aggressiveness.

Phenotypic Characterization by Mass Cytometry of the Microenvironment in Ovarian Cancer and Impact of Tumor Dissociation Methods

Simple Summary

High-grade serous ovarian cancer (HGSOC) is the deadliest gynecological malignancy. Despite increasing research on HGSOC, biomarkers for individualized selection of therapy are scarce. In this study, we develop a multiparametric mass cytometry antibody panel to identify differences in the cellular composition of the microenvironment of tumor tissues dissociated to single-cell suspensions. We also investigate how dissociation methods impact results. Application of our antibody panel to HGSOC tissues showed its ability to identify established main cell subsets and subpopulations of these cells. Comparisons between dissociation methods revealed differences in cell fractions for one immune, two stromal, and three tumor cell subpopulations, while functional marker expression was not affected by the dissociation method. The interpatient disparities identified in the tumor microenvironment were more significant than those identified between differently dissociated tissues from one patient, indicating that the panel facilitates the mapping of individual tumor microenvironments in HGSOC patients.

Abstract

Improved molecular dissection of the tumor microenvironment (TME) holds promise for treating high-grade serous ovarian cancer (HGSOC), a gynecological malignancy with high mortality. Reliable disease-related biomarkers are scarce, but single-cell mapping of the TME could identify patient-specific prognostic differences. To avoid technical variation effects, however, tissue dissociation effects on single cells must be considered. We present a novel Cytometry by Time-of-Flight antibody panel for single-cell suspensions to identify individual TME profiles of HGSOC patients and evaluate the effects of dissociation methods on results. The panel was developed utilizing cell lines, healthy donor blood, and stem cells and was applied to HGSOC tissues dissociated by six methods. Data were analyzed using Cytobank and X-shift and illustrated by t-distributed stochastic neighbor embedding plots, heatmaps, and stacked bar and error plots. The panel distinguishes the main cellular subsets and subpopulations, enabling characterization of individual TME profiles. The dissociation method affected some immune (n = 1), stromal (n = 2), and tumor (n = 3) subsets, while functional marker expressions remained comparable. In conclusion, the panel can identify subsets of the HGSOC TME and can be used for in-depth profiling. This panel represents a promising profiling tool for HGSOC when tissue handling is considered.

Identification of the Hub Genes Associated with the Prognosis of Ovarian Cancer Patients via Integrated Bioinformatics Analysis and Experimental Validation

Background

This study aimed to identify the hub genes associated with prognosis of patients with ovarian cancer by using integrated bioinformatics analysis and experimental validation.

Methods

Four microarray datasets (GSE12470, GSE14407, GSE18521 and GSE46169) were analyzed by the GEO2R tool to screen common differentially expressed genes (DEGs). Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, the (KEGG) pathway and Reactome pathway enrichment analysis, protein–protein interaction (PPI) construction, and the identification of hub genes were performed. Furthermore, we performed the survival and expression analysis of the hub genes. In vitro functional assays were performed to assess the effects of hub genes on ovarian cancer cell proliferation, caspase-3/7 activity and invasion.

Results

A total of 89 common DEGs were identified among these four datasets. The KEGG and Reactome pathway results showed that the DEGs were mainly associated with cell cycle, mitotic and p53 signaling pathway. A total of 20 hub genes were identified from the PPI network by using sub-module analysis. The survival analysis revealed that high expression of six hub genes (AURKA, BUB1B, CENPF, KIF11, KIF23 and TOP2A) were significantly correlated with shorter overall survival and progression-free survival of patients with ovarian cancer. Furthermore, the expression of the six hub genes were validated by the GEPIA database and Human Protein Atlas, and functional studies revealed that knockdown of KIF11 and KIF23 suppressed the SKOV3 cell proliferation, increased caspase-3/7 activity and attenuated invasive potentials of SKOV3 cells. In addition, knockdown of KIF11 and KIF23 up-regulated E-cadherin mRNA expression but down-regulated N-cadherin and vimentin mRNA expression in SKOV3 cells.

Conclusion

Our results showed that six hub genes were up-regulated in ovarian cancer tissues and may predict poor prognosis of patients with ovarian cancer. KIF11 and KIF23 may play oncogenic roles in ovarian cancer cell progression via promoting ovarian cancer cell proliferation and invasion.

Biomarkers for Precision Urothelial Carcinoma Diagnosis: Current Approaches and the Application of Single-Cell Technologies

Simple Summary

Urothelial carcinoma (UC) is the most frequently diagnosed cancer of the urinary tract and is ranked the sixth most diagnosed cancer in men worldwide. About 70–75% of newly diagnosed UCs are non-invasive or low grade. Different tests such as urine cytology and cystoscopy are used to detect UC. If abnormal tissue is found during cystoscopy, then a biopsy will be performed. Cytology has low sensitivity for low-grade cancer while cystoscopy is invasive and costly. Detecting UC early improves the chances of treatment success. Therefore, many researchers have painstakingly identified urine biological markers for non-invasive UC diagnosis. In this review, we summarize some of the latest and most promising biological markers (including FDA-approved and investigational markers). We also discuss some new technologies that can aid research efforts in biological marker discovery for early UC detection.

Abstract

Urothelial carcinoma (UC) is the most frequent malignancy of the urinary system and is ranked the sixth most diagnosed cancer in men worldwide. Around 70–75% of newly diagnosed UC manifests as the non-muscle invasive bladder cancer (NMIBC) subtype, which can be treated by a transurethral resection of the tumor. However, patients require life-long monitoring due to its high rate of recurrence. The current gold standard for UC diagnosis, prognosis, and disease surveillance relies on a combination of cytology and cystoscopy, which is invasive, costly, and associated with comorbidities. Hence, there is considerable interest in the development of highly specific and sensitive urinary biomarkers for the non-invasive early detection of UC. In this review, we assess the performance of current diagnostic assays for UC and highlight some of the most promising biomarkers investigated to date. We also highlight some of the recent advances in single-cell technologies that may offer a paradigm shift in the field of UC biomarker discovery and precision diagnostics.

Single-Cell Sequencing and Organoids: A Powerful Combination for Modelling Organ Development and Diseases

The development and function of a particular organ and the pathogenesis of various diseases remain intimately linked to the features of each cell type in the organ. Conventional messenger RNA- or protein-based methodologies often fail to elucidate the contribution of rare cell types, including some subpopulations of stem cells, short-lived progenitors and circulating tumour cells, thus hampering their applications in studies regarding organ development and diseases. The scRNA-seq technique represents a new approach for determining gene expression variability at the single-cell level. Organoids are new preclinical models that recapitulate complete or partial features of their original organ and are thought to be superior to cell models in mimicking the sophisticated spatiotemporal processes of the development and regeneration and diseases. In this review, we highlight recent advances in the field of scRNA-seq, organoids and their current applications and summarize the advantages of using a combination of scRNA-seq and organoid technology to model diseases and organ development.

Single-Cell Analysis of Different Stages of Oral Cancer Carcinogenesis in a Mouse Model

Oral carcinogenesis involves the progression of the normal mucosa into potentially malignant disorders and finally into cancer. Tumors are heterogeneous, with different clusters of cells expressing different genes and exhibiting different behaviors. 4-nitroquinoline 1-oxide (4-NQO) and arecoline were used to induce oral cancer in mice, and the main factors for gene expression influencing carcinogenesis were identified through single-cell RNA sequencing analysis. Male C57BL/6J mice were divided into two groups: a control group (receiving normal drinking water) and treatment group (receiving drinking water containing 4-NQO (200 mg/L) and arecoline (500 mg/L)) to induce the malignant development of oral cancer. Mice were sacrificed at 8, 16, 20, and 29 weeks. Except for mice sacrificed at 8 weeks, all mice were treated for 16 weeks and then either sacrificed or given normal drinking water for the remaining weeks. Tongue lesions were excised, and all cells obtained from mice in the 29- and 16-week treatment groups were clustered into 17 groups by using the Louvain algorithm. Cells in subtypes 7 (stem cells) and 9 (keratinocytes) were analyzed through gene set enrichment analysis. Results indicated that their genes were associated with the MYC_targets_v1 pathway, and this finding was confirmed by the presence of cisplatin-resistant nasopharyngeal carcinoma cell lines. These cell subtype biomarkers can be applied for the detection of patients with precancerous lesions, the identification of high-risk populations, and as a treatment target.

Ovarian cancer and the evolution of subtype classifications using transcriptional profiling†

Ovarian cancer is a complex disease with multiple subtypes, each having distinct histopathologies and variable responses to treatment. This review highlights the technological milestones and the studies that have applied them to change our definitions of ovarian cancer. Over the past 50 years, technologies such as microarrays and next-generation sequencing have led to the discovery of molecular alterations that define each of the ovarian cancer subtypes and has enabled further subclassification of the most common subtype, high-grade serous ovarian cancer (HGSOC). Improvements in mutational profiling have provided valuable insight, such as the ubiquity of TP53 mutations in HGSOC tumors. However, the information derived from these technological advances has also revealed the immense heterogeneity of this disease, from variation between patients to compositional differences within single masses. In looking forward, the emerging technologies for single-cell and spatially resolved transcriptomics will allow us to better understand the cellular composition and structure of tumors and how these contribute to the molecular subtypes. Attempts to incorporate the complexities ovarian cancer has resulted in increasing sophistication of model systems, and the increased precision in molecular profiling of ovarian cancers has already led to the introduction of inhibitors of poly (ADP-ribose) polymerases as a new class of treatments for ovarian cancer with DNA repair deficiencies. Future endeavors to define increasingly accurate classification strategies for ovarian cancer subtypes will allow for confident prediction of disease progression and provide important insight into potentially targetable molecular mechanisms specific to each subtype.

Applying Single-Cell Analysis to Gonadogenesis and DSDs (Disorders/Differences of Sex Development)

The gonads are unique among the body's organs in having a developmental choice: testis or ovary formation. Gonadal sex differentiation involves common progenitor cells that form either Sertoli and Leydig cells in the testis or granulosa and thecal cells in the ovary. Single-cell analysis is now shedding new light on how these cell lineages are specified and how they interact with the germline. Such studies are also providing new information on gonadal maturation, ageing and the somatic-germ cell niche. Furthermore, they have the potential to improve our understanding and diagnosis of Disorders/Differences of Sex Development (DSDs). DSDs occur when chromosomal, gonadal or anatomical sex are atypical. Despite major advances in recent years, most cases of DSD still cannot be explained at the molecular level. This presents a major pediatric concern. The emergence of single-cell genomics and transcriptomics now presents a novel avenue for DSD analysis, for both diagnosis and for understanding the molecular genetic etiology. Such -omics datasets have the potential to enhance our understanding of the cellular origins and pathogenesis of DSDs, as well as infertility and gonadal diseases such as cancer.