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

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.

Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma

Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but their interpretation and translation are complicated by tumor evolution and polyclonality accompanied by extensive accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. In this study, we examined the chronology of HGSOC subtype evolution in the context of these factors using a novel integrative analysis of absolute copy-number analysis and gene expression in The Cancer Genome Atlas complemented by single-cell analysis of six independent tumors. Tumor purity, ploidy, and subclonality were reliably inferred from different genomic platforms, and these characteristics displayed marked differences between subtypes. Genomic lesions associated with HGSOC subtypes tended to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations was evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors were characterized by largely intact genome integrity, high immune infiltration, and younger patient age. Single-cell sequencing of 42,000 tumor cells revealed widespread heterogeneity in tumor cell type composition that drove bulk subtypes but demonstrated a lack of intrinsic subtypes among tumor epithelial cells. Our findings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tumor stage and tissue of origin, and evolution between properties previously associated with discrete subtypes. SIGNIFICANCE: This study infers whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are, therefore, appropriate targets for therapy and demonstrates that this is not the case for HGSOC.

A single-cell landscape of high-grade serous ovarian cancer

Malignant abdominal fluid (ascites) frequently develops in women with advanced high-grade serous ovarian cancer (HGSOC) and is associated with drug resistance and a poor prognosis1. To comprehensively characterize the HGSOC ascites ecosystem, we used single-cell RNA sequencing to profile ~11,000 cells from 22 ascites specimens from 11 patients with HGSOC. We found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. We found that the previously described immunoreactive and mesenchymal subtypes of HGSOC, which have prognostic implications, reflect the abundance of immune infiltrates and fibroblasts rather than distinct subsets of malignant cells2. Malignant cell variability was partly explained by heterogeneous copy number alteration patterns or expression of a stemness program. Malignant cells shared expression of inflammatory programs that were largely recapitulated in single-cell RNA sequencing of ~35,000 cells from additionally collected samples, including three ascites, two primary HGSOC tumors and three patient ascites-derived xenograft models. Inhibition of the JAK/STAT pathway, which was expressed in both malignant cells and cancer-associated fibroblasts, had potent anti-tumor activity in primary short-term cultures and patient-derived xenograft models. Our work contributes to resolving the HSGOC landscape3-5 and provides a resource for the development of novel therapeutic approaches.

Single-cell analysis on stromal fibroblasts in the microenvironment of solid tumours

Besides malignant cells, the tumour microenvironment consists of various stromal cells such as cancer-associated fibroblasts (CAFs) and myofibroblasts. Accumulation of heterogeneous populations of stromal cells in solid tumours is associated with lower survival rates and cancer recurrence in patients. Certain limitations presented by conventional experimental designs and techniques in cancer research have led to poor understanding of the fundamental basis of cancer niche. Recent developments in single-cell techniques allow more in-depth studies of the tumour microenvironment. Analyses at the single-cell level enables the detection of rare cell types, characterization of intra-tumour cellular heterogeneity and analysis of the lineage output of malignant cells. This subsequently, provides valuable insights on better diagnostic methods and treatment avenues for cancer. This review explores the recent advancements and applications of single-cell technologies in cancer research pertaining to the study of stromal fibroblasts in the microenvironment of solid tumours.

Single-cell transcriptomics of blood reveals a natural killer cell subset depletion in tuberculosis

Background

Tuberculosis (TB) continues to be a critical global health problem, which killed millions of lives each year. Certain circulating cell subsets are thought to differentially modulate the host immune response towards Mycobacterium tuberculosis (Mtb) infection, but the nature and function of these subsets is unclear.

Methods

Peripheral blood mononuclear cells (PBMC) were isolated from healthy controls (HC), latent tuberculosis infection (LTBI) and active tuberculosis (TB) and then subjected to single-cell RNA sequencing (scRNA-seq) using 10 × Genomics platform. Unsupervised clustering of the cells based on the gene expression profiles using the Seurat package and passed to tSNE for clustering visualization. Flow cytometry was used to validate the subsets identified by scRNA-Seq.

Findings

Cluster analysis based on differential gene expression revealed both known and novel markers for all main PBMC cell types and delineated 29 cell subsets. By comparing the scRNA-seq datasets from HC, LTBI and TB, we found that infection changes the frequency of immune-cell subsets in TB. Specifically, we observed gradual depletion of a natural killer (NK) cell subset (CD3-CD7+GZMB+) from HC, to LTBI and TB. We further verified that the depletion of CD3-CD7+GZMB+ subset in TB and found an increase in this subset frequency after anti-TB treatment. Finally, we confirmed that changes in this subset frequency can distinguish patients with TB from LTBI and HC.

Interpretation

We propose that the frequency of CD3-CD7+GZMB+ in peripheral blood could be used as a novel biomarker for distinguishing TB from LTBI and HC.

Fund

The study was supported by Natural Science Foundation of China (81770013, 81525016, 81772145, 81871255 and 91942315), National Science and Technology Major Project (2017ZX10201301), Science and Technology Project of Shenzhen (JCYJ20170412101048337) and Guangdong Provincial Key Laboratory of Regional Immunity and Diseases (2019B030301009). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Ovarian cancer-associated mesothelial cells induce acquired platinum-resistance in peritoneal metastasis via the FN1/Akt signaling pathway

Peritoneal dissemination of ovarian cancer (OvCa) arises from the surface of the peritoneum, covered by monolayer of mesothelial cells (MCs). Given that both OvCa cells and MCs are present in the same peritoneal metastatic microenvironment, they may establish cell-to-cell crosstalk or phenotypic alterations including the acquisition of platinum-resistance in OvCa cells. Herein, we report how OvCa-associated mesothelial cells (OCAMs) induce platinum-resistance in OvCa cells through direct cell-to-cell crosstalk. We evaluated mutual associations between OvCa cells and human primary MCs with in vitro coculturing experimental models and in silico omics data analysis. The role of OCAMs was also investigated using clinical samples and in vivo mice models. Results of in vitro experiments show that mesenchymal transition is induced in OCAMs primarily by TGF-β1 stimulation. Furthermore, OCAMs influence the behavior of OvCa cells as a component of the tumor microenvironment of peritoneal metastasis. Mechanistically, OCAMs can induce decreased platinum-sensitivity in OvCa cells via induction of the FN1/Akt signaling pathway via cell-to-cell interactions. Histological analysis of OvCa peritoneal metastasis also illustrated FN1 expression in stromal cells that are supposed to originate from MCs. Further, we also confirmed the activation of Akt signaling in OvCa cells in contact with TGF-β1 stimulated peritoneum, using an in vivo mice model. Our results suggest that the tumor microenvironment, enhanced by direct cell-to-cell crosstalk between OvCa cells and OCAMs, induces acquisition of platinum-resistance in OvCa cells, which may serve as a novel therapeutic target for prevention of OvCa peritoneal dissemination.

Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies

Effective cancer treatment has been precluded by the presence of various forms of intratumoral complexity that drive treatment resistance and metastasis. Recent single-cell sequencing technologies are significantly facilitating the characterization of tumor internal architecture during disease progression. New applications and advances occurring at a fast pace predict an imminent broad application of these technologies in many research areas. As occurred with next-generation sequencing (NGS) technologies, once applied to clinical samples across tumor types, single-cell sequencing technologies could trigger an exponential increase in knowledge of the molecular pathways involved in cancer progression and contribute to the improvement of cancer treatment.

Systems Immunology: Learning the Rules of the Immune System

Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.

De novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments

This study describes a computational method for determining statistical support to varying levels of heterogeneity provided by single-cell RNA-sequencing data with applications to tumor samples.

Recent single-cell transcriptomic studies revealed new insights into cell-type heterogeneities in cellular microenvironments unavailable from bulk studies. A significant drawback of currently available algorithms is the need to use empirical parameters or rely on indirect quality measures to estimate the degree of complexity, i.e., the number of subgroups present in the sample. We fill this gap with a single-cell data analysis procedure allowing for unambiguous assessments of the depth of heterogeneity in subclonal compositions supported by data. Our approach combines nonnegative matrix factorization, which takes advantage of the sparse and nonnegative nature of single-cell RNA count data, with Bayesian model comparison enabling de novo prediction of the depth of heterogeneity. We show that the method predicts the correct number of subgroups using simulated data, primary blood mononuclear cell, and pancreatic cell data. We applied our approach to a collection of single-cell tumor samples and found two qualitatively distinct classes of cell-type heterogeneity in cancer microenvironments.

Application of single-cell sequencing in human cancer

Precision medicine is emerging as a cornerstone of future cancer care with the objective of providing targeted therapies based on the molecular phenotype of each individual patient. Traditional bulk-level molecular phenotyping of tumours leads to significant information loss, as the molecular profile represents an average phenotype over large numbers of cells, while cancer is a disease with inherent intra-tumour heterogeneity at the cellular level caused by several factors, including clonal evolution, tissue hierarchies, rare cells and dynamic cell states. Single-cell sequencing provides means to characterize heterogeneity in a large population of cells and opens up opportunity to determine key molecular properties that influence clinical outcomes, including prognosis and probability of treatment response. Single-cell sequencing methods are now reliable enough to be used in many research laboratories, and we are starting to see applications of these technologies for characterization of human primary cancer cells. In this review, we provide an overview of studies that have applied single-cell sequencing to characterize human cancers at the single-cell level, and we discuss some of the current challenges in the field.

Biological Insights into Chemotherapy Resistance in Ovarian Cancer

The majority of patients with high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy; however, most will develop chemotherapy resistance. Gene signatures may change with the development of chemotherapy resistance in this population, which is important as it may lead to tailored therapies. The objective of this study was to compare tumor gene expression profiles in patients before and after treatment with neoadjuvant chemotherapy (NACT). Tumor samples were collected from six patients diagnosed with HGSOC before and after administration of NACT. RNA extraction and whole transcriptome sequencing was performed. Differential gene expression, hierarchical clustering, gene set enrichment analysis, and pathway analysis were examined in all of the samples. Tumor samples clustered based on exposure to chemotherapy as opposed to patient source. Pre-NACT samples were enriched for multiple pathways involving cell cycle growth. Post-NACT samples were enriched for drug transport and peroxisome pathways. Molecular subtypes based on the pre-NACT sample (differentiated, mesenchymal, proliferative and immunoreactive) changed in four patients after administration of NACT. Multiple changes in tumor gene expression profiles after exposure to NACT were identified from this pilot study and warrant further attention as they may indicate early changes in the development of chemotherapy resistance.

Revisiting Telomere Shortening in Cancer

Telomeres, the protective structures of chromosome ends are gradually shortened by each cell division, eventually leading to senescence or apoptosis. Cancer cells maintain the telomere length for unlimited growth by telomerase reactivation or a recombination-based mechanism. Recent genome-wide analyses have unveiled genetic and epigenetic alterations of the telomere maintenance machinery in cancer. While telomerase inhibition reveals that longer telomeres are more advantageous for cell survival, cancer cells often have paradoxically shorter telomeres compared with those found in the normal tissues. In this review, we summarize the latest knowledge about telomere length alterations in cancer and revisit its rationality. Finally, we discuss the potential utility of telomere length as a prognostic biomarker.

Decoding transcriptomic intra-tumour heterogeneity to guide personalised medicine in ovarian cancer

The evaluation of intra-tumour heterogeneity (ITH) from a transcriptomic point of view is limited. Single-cell cancer studies reveal significant genomic and transcriptomic ITH within a tumour and it is no longer adequate to employ single-subtype assignment as this does not acknowledge the ITH that exists. Molecular assessment of subtype heterogeneity (MASH) was developed to comprehensively report on the composition of all transcriptomic subtypes within a tumour lesion. Using MASH on 3431 ovarian cancer samples, correlation and association analyses with survival, metastasis and clinical outcomes were performed to assess the impact of subtype composition as a surrogate for ITH. The association was validated on two independent cohorts. We identified that 30% of ovarian tumours consist of two or more subtypes. When biological features of the subtype constituents were examined, we identified significant impact on clinical outcomes with the presence of poor prognostic subtypes (Mes or Stem-A). Poorer outcomes correlated with having higher degrees of poor prognostic subtype populations within the tumour. Subtype prediction in several independent datasets reflected a similar prognostic trend. In addition, paired analysis of primary and recurrent/metastatic tumours demonstrated Mes and/or Stem-A subtypes predominated in recurrent and metastatic tumours regardless of the original primary subtype. Given the biological and prognostic value in delineating individual subtypes within a tumour, a clinically applicable MASH assay using NanoString^® technology was developed as a classification tool to comprehensively describe constituents of molecular subtypes. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Single-cell analysis of tumors: Creating new value for molecular biomarker discovery of cancer stem cells and tumor-infiltrating immune cells

Biomarker-driven individualized treatment in oncology has made tremendous progress through technological developments, new therapeutic modalities and a deeper understanding of the molecular biology for tumors, cancer stem cells and tumor-infiltrating immune cells. Recent technical developments have led to the establishment of a variety of cancer-related diagnostic, prognostic and predictive biomarkers. In this regard, different modern OMICs approaches were assessed in order to categorize and classify prognostically different forms of neoplasia. Despite those technical advancements, the extent of molecular heterogeneity at the individual cell level in human tumors remains largely uncharacterized. Each tumor consists of a mixture of heterogeneous cell types. Therefore, it is important to quantify the dynamic cellular variations in order to predict clinical parameters, such as a response to treatment and or potential for disease recurrence. Recently, single-cell based methods have been developed to characterize the heterogeneity in seemingly homogenous cancer cell populations prior to and during treatment. In this review, we highlight the recent advances for single-cell analysis and discuss the challenges and prospects for molecular characterization of cancer cells, cancer stem cells and tumor-infiltrating immune cells.

Single-cell RNA-sequencing reveals transcriptional dynamics of estrogen-induced dysplasia in the ovarian surface epithelium

Estrogen therapy increases the risk of ovarian cancer and exogenous estradiol accelerates the onset of ovarian cancer in mouse models. Both in vivo and in vitro, ovarian surface epithelial (OSE) cells exposed to estradiol develop a subpopulation that loses cell polarity, contact inhibition, and forms multi-layered foci of dysplastic cells with increased susceptibility to transformation. Here, we use single-cell RNA-sequencing to characterize this dysplastic subpopulation and identify the transcriptional dynamics involved in its emergence. Estradiol-treated cells were characterized by up-regulation of genes associated with proliferation, metabolism, and survival pathways. Pseudotemporal ordering revealed that OSE cells occupy a largely linear phenotypic spectrum that, in estradiol-treated cells, diverges towards cell state consistent with the dysplastic population. This divergence is characterized by the activation of various cancer-associated pathways including an increase in Greb1 which was validated in fallopian tube epithelium and human ovarian cancers. Taken together, this work reveals possible mechanisms by which estradiol increases epithelial cell susceptibility to tumour initiation.

Identification of grade and origin specific cell populations in serous epithelial ovarian cancer by single cell RNA-seq

Here we investigated different cell populations within ovarian cancer using single-cell RNA seq: fourteen samples from nine patients with differing grades (high grade, low grade and benign) as well as different origin sites (primary and metastatic tumor site, ovarian in origin and fallopian in origin). We were able to identify sixteen distinct cell populations with specific cells correlated to high grade tumors, low grade tumors, benign and one population unique to a patient with a breast cancer relapse. Furthermore the proportion of these populations changes from primary to metastatic in a shift from mainly epithelial cells to leukocytes with few cancer epithelial cells in the metastases. Differential gene expression shows myeloid lineage cells are the primary cell group expressing soluble factors in primary samples while fibroblasts do so in metastatic samples. The leukocytes that were captured did not seem to be suppressed through known pro-tumor cytokines from any of the cell populations. Single cell RNA-seq is necessary to de-tangle cellular heterogeneity for better understanding of ovarian cancer progression.

RNA sequencing (RNA-Seq) and its application in ovarian cancer

Despite the surgical and chemotherapeutic advances over the past few decades, ovarian cancer remains the leading cause of gynecological cancer-related mortality. The absence of biomarkers in early detection and the development of drug resistance are principal causes of treatment failure in ovarian cancer. Recent progress in RNA sequencing (RNA-Seq) with Next Generation Sequencing technology has expanded the understanding of the molecular pathogenesis of ovarian cancer. As compared to previous hybridization-based microarray and Sanger sequence-based methods, RNA-Seq provides multiple layers of resolutions and transcriptome complexity, with less background noise and a broader dynamic range of RNA expression. Beyond quantifying gene expression, the data generated by RNA-Seq accelerates the identification of alternatively spliced genes, fusion genes, mutations/SNPs, allele-specific expression, novel transcripts and non-coding RNAs. RNA-Seq has been successfully applied in ovarian cancer research for earlier detection, ascertaining pathological origin, and defining the aberrant genes and dysregulated molecular pathways across patient groups. This review outlines the distinct advantages of RNA-Seq compared to other transcriptomics methods and its recent applications in ovarian cancer.

Characterization of stem cell and cancer stem cell populations in ovary and ovarian tumors

Background

Ovarian cancer is a complicated malady associated with cancer stem cells (CSCs) contributing to 238,700 estimated new cases and 151,900 deaths per year, worldwide. CSCs comprise a tiny fraction of tumor-bulk responsible for cancer recurrence and eventual mortality. CSCs or tumor initiating cells are responsible for self-renewal, differentiation and proliferative potential, tumor initiation capability, its progression, drug resistance and metastatic spread. Although several biomarkers are implicated in these processes, their distribution within the ovary and association with single cell type has neither been established nor demonstrated across ovarian tumor developmental stages. Therefore, precise identification, thorough characterization and effective targeted destruction of dormant and highly proliferating potent CSC populations is an immediate need.

Results

In view of this, distribution of various CSC (ALDH1/2, C-KIT, CD133, CD24 and CD44) and cell proliferation (KI67) specific markers in the ovarian surface epithelium (OSE) and cortex regions in normal ovary, and benign, borderline and high grade metastatic ovarian tumors by immuno-histochemistry and confocal microscopy was studied. We further confirmed their expression by RT-PCR analysis. Co-expression analysis of stem cell (OCT4, SSEA4) and CSC (ALDH1/2, CD44 and LGR5) markers with proliferation marker (KI67) in HG tumors revealed dual positive proliferating stem and CSCs, few non-proliferating stem/CSC (SSEA4⁺/KI67⁻ and ALDH1/2⁺/KI67⁻) and only KI67⁺ cells in cortex, signifying dynamic populations and interesting cellular hierarchy in cortex region. Smaller spherical (≤ 5 μm) and larger spindle/elliptical shaped (~ 10 μm) cell populations with high nucleo-cytoplasmic ratio were detected across all samples (including normal ovaries) but with variable distribution and characteristic stage-wise marker expression across different tumor stages.

Conclusions

Diverse stem and CSC populations expressing characteristic markers revealing distinct phenotypes (spherical ≤5 μm and spindle/elliptical ~ 10 μm) were distributed within different tumor stages studied signifying dynamic and probable functional hierarchy within these cell types. Involvement of extra-ovarian sites of origin of stem and CSCs requires rigorous evaluation. Quantitative analysis of potent CSC populations, their mechanisms and pathways for self-renewal, chemo-resistance, metastatic spread etc. with respect to various markers studied, will provide better insights and targets for developing effective therapeutics to prevent metastasis and eventually help improve patient mortality.

Electronic supplementary material

The online version of this article (10.1186/s13048-018-0439-3) contains supplementary material, which is available to authorized users.

Semi-supervised identification of cancer subgroups using survival outcomes and overlapping grouping information

Identification of cancer patient subgroups using high throughput genomic data is of critical importance to clinicians and scientists because it can offer opportunities for more personalized treatment and overlapping treatments of cancers. In spite of tremendous efforts, this problem still remains challenging because of low reproducibility and instability of identified cancer subgroups and molecular features. In order to address this challenge, we developed Integrative Genomics Robust iDentification of cancer subgroups (InGRiD), a statistical approach that integrates information from biological pathway databases with high-throughput genomic data to improve the robustness for identification and interpretation of molecularly-defined subgroups of cancer patients. We applied InGRiD to the gene expression data of high-grade serous ovarian cancer from The Cancer Genome Atlas and the Australian Ovarian Cancer Study. The results indicate clear benefits of the pathway-level approaches over the gene-level approaches. In addition, using the proposed InGRiD framework, we also investigate and address the issue of gene sharing among pathways, which often occurs in practice, to further facilitate biological interpretation of key molecular features associated with cancer progression. The R package "InGRiD" implementing the proposed approach is currently available in our research group GitHub webpage ( https://dongjunchung.github.io/INGRID/ ).

Application of Single Cell Sequencing in Cancer

Cancer is a heterogenetic disease at both the level of clinical manifestation and the level of the genome. Single-cell sequencing provides an unprecedented means of characterizing the intra-tumor heterogeneity and detecting and analyzing the genomes of cancer cells. These data will help to reconstruct the understanding of the evolutionary lineage of cancer cells. In the future, single-cell technology is believed to be a useful tool in diagnostic and prognostic application in oncology. The application of single cell technology in clinics will make it possible to detect cancer non-invasively at early stages and to develop precision medicine. In this chapter, we review the research and application status of the single cell technology in cancer.

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes

PURPOSE OF REVIEW

Only recently has it become clear that epithelial ovarian cancer (EOC) is comprised of such distinct histotypes--with different cells of origin, morphology, molecular features, epidemiologic factors, clinical features, and survival patterns-that they can be thought of as different diseases sharing an anatomical location. Herein, we review opportunities and challenges in studying EOC heterogeneity.

RECENT FINDINGS

The 2014 World Health Organization diagnostic guidelines incorporate accumulated evidence that high- and low-grade serous tumors have different underlying pathogenesis, and that, on the basis of shared molecular features, most high grade tumors, including some previously classified as endometrioid, are now considered to be high-grade serous. At the same time, several studies have reported that high-grade serous EOC, which is the most common histotype, is itself made up of reproducible subtypes discernable by gene expression patterns.

SUMMARY

These major advances in understanding set the stage for a new era of research on EOC risk and clinical outcomes with the potential to reduce morbidity and mortality. We highlight the need for multidisciplinary studies with pathology review using the current guidelines, further molecular characterization of the histotypes and subtypes, inclusion of women of diverse racial/ethnic and socioeconomic backgrounds, and updated epidemiologic and clinical data relevant to current generations of women at risk of EOC.

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes

Purpose of review

Only recently has it become clear that epithelial ovarian cancer (EOC) is comprised of such distinct histotypes--with different cells of origin, morphology, molecular features, epidemiologic factors, clinical features, and survival patterns-that they can be thought of as different diseases sharing an anatomical location. Herein, we review opportunities and challenges in studying EOC heterogeneity.

Recent findings

The 2014 World Health Organization diagnostic guidelines incorporate accumulated evidence that high- and low-grade serous tumors have different underlying pathogenesis, and that, on the basis of shared molecular features, most high grade tumors, including some previously classified as endometrioid, are now considered to be high-grade serous. At the same time, several studies have reported that high-grade serous EOC, which is the most common histotype, is itself made up of reproducible subtypes discernable by gene expression patterns.

Summary

These major advances in understanding set the stage for a new era of research on EOC risk and clinical outcomes with the potential to reduce morbidity and mortality. We highlight the need for multidisciplinary studies with pathology review using the current guidelines, further molecular characterization of the histotypes and subtypes, inclusion of women of diverse racial/ethnic and socioeconomic backgrounds, and updated epidemiologic and clinical data relevant to current generations of women at risk of EOC.

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes

PURPOSE OF REVIEW

Only recently has it become clear that epithelial ovarian cancer (EOC) is comprised of such distinct histotypes--with different cells of origin, morphology, molecular features, epidemiologic factors, clinical features, and survival patterns-that they can be thought of as different diseases sharing an anatomical location. Herein, we review opportunities and challenges in studying EOC heterogeneity.

RECENT FINDINGS

The 2014 World Health Organization diagnostic guidelines incorporate accumulated evidence that high- and low-grade serous tumors have different underlying pathogenesis, and that, on the basis of shared molecular features, most high grade tumors, including some previously classified as endometrioid, are now considered to be high-grade serous. At the same time, several studies have reported that high-grade serous EOC, which is the most common histotype, is itself made up of reproducible subtypes discernable by gene expression patterns.

SUMMARY

These major advances in understanding set the stage for a new era of research on EOC risk and clinical outcomes with the potential to reduce morbidity and mortality. We highlight the need for multidisciplinary studies with pathology review using the current guidelines, further molecular characterization of the histotypes and subtypes, inclusion of women of diverse racial/ethnic and socioeconomic backgrounds, and updated epidemiologic and clinical data relevant to current generations of women at risk of EOC.

Ovarian cancer: Novel molecular aspects for clinical assessment

Ovarian cancer is a very heterogeneous tumor which has been traditionally characterized according to the different histological subtypes and differentiation degree. In recent years, innovative molecular screening biotechnologies have allowed to identify further subtypes of this cancer based on gene expression profiles, mutational features, and epigenetic factors. These novel classification systems emphasizing the molecular signatures within the broad spectrum of ovarian cancer have not only allowed a more precise prognostic prediction, but also proper therapeutic strategies for specific subgroups of patients. The bulk of available scientific data and the high refinement of molecular classifications of ovarian cancers can today address the research towards innovative drugs with the adoption of targeted therapies tailored for single molecular profiles leading to a better prediction of therapeutic response. Here, we summarize the current state of knowledge on the molecular bases of ovarian cancer, from the description of its molecular subtypes derived from wide high-throughput analyses to the latest discoveries of the ovarian cancer stem cells. The latest personalized treatment options are also presented with recent advances in using PARP inhibitors, anti-angiogenic, anti-folate receptor and anti-cancer stem cells treatment approaches.

High-dimension single-cell analysis applied to cancer

High-dimension single-cell technology is transforming our ability to study and understand cancer. Numerous studies and reviews have reported advances in technology development. The biological insights gleaned from single-cell technology about cancer biology are less reviewed. Here we focus on research studies that illustrate novel aspects of cancer biology that bulk analysis could not achieve, and discuss the fresh insights gained from the application of single-cell technology across basic and clinical cancer studies.

Single-Cell mRNA Sequencing in Cancer Research: Integrating the Genomic Fingerprint

Critical cancer mutations are often regional and mosaic, confounding the efficacy of targeted therapeutics. Single cell mRNA sequencing (scRNA-seq) has enabled unprecedented studies of intra-tumor heterogeneity and its role in cancer progression, metastasis, and treatment resistance. When coupled with DNA sequencing, scRNA-seq allows one to infer the in vivo impact of genomic alterations on gene expression. This combination can be used to reliably distinguish neoplastic from non-neoplastic cells, to correlate paracrine-signaling pathways between neoplastic cells and stroma, and to map expression signatures to inferred clones and phylogenies. Here we review recent advances in scRNA-seq, with a special focus on cancer. We discuss the challenges and prospects of combining scRNA-seq with DNA sequencing to assess intra-tumor heterogeneity.

Application of single-cell technology in cancer research

In this review, we have outlined the application of single-cell technology in cancer research. Single-cell technology has made encouraging progress in recent years and now provides the means to detect rare cancer cells such as circulating tumor cells and cancer stem cells. We reveal how this technology has advanced the analysis of intratumor heterogeneity and tumor epigenetics, and guided individualized treatment strategies. The future prospects now are to bring single-cell technology into the clinical arena. We believe that the clinical application of single-cell technology will be beneficial in cancer diagnostics and treatment, and ultimately improve survival in cancer patients.