Commonly Occurring Cell Subsets in High-Grade Serous Ovarian Tumors Identified by Single-Cell Mass Cytometry

ATF3 characterizes aggressive drug-tolerant persister cells in HGSOC

High-grade serous ovarian cancer (HGSOC) represents the most common and lethal subtype of ovarian cancer. Despite initial response to platinum-based standard therapy, patients commonly suffer from relapse that likely originates from drug-tolerant persister (DTP) cells. We generated isogenic clones of treatment-naïve and cisplatin-tolerant persister HGSOC cells. In addition, single-cell RNA sequencing of barcoded cells was performed in a xenograft model with HGSOC cell lines after platinum-based therapy. Published single-cell RNA-sequencing data from neo-adjuvant and non-treated HGSOC patients and patient data from TCGA were analyzed. DTP-derived cells exhibited morphological alterations and upregulation of epithelial-mesenchymal transition (EMT) markers. An aggressive subpopulation of DTP-derived cells showed high expression of the stress marker ATF3. Knockdown of ATF3 enhanced the sensitivity of aggressive DTP-derived cells to cisplatin-induced cell death, implying a role for ATF3 stress response in promoting a drug tolerant persister cell state. Furthermore, single cell lineage tracing to detect transcriptional changes in a HGSOC cell line-derived xenograft relapse model showed that cells derived from relapsed solid tumors express increased levels of EMT and multiple endoplasmic reticulum (ER) stress markers, including ATF3. Single cell RNA sequencing of epithelial cells from four HGSOC patients also identified a small cell population resembling DTP cells in all samples. Moreover, analysis of TCGA data from 259 HGSOC patients revealed a significant progression-free survival advantage for patients with low expression of the ATF3-associated partial EMT genes. These findings suggest that increased ATF3 expression together with partial EMT promote the development of aggressive DTP, and thereby relapse in HGSOC patients.

Identification and characterization of intermediate states in mammalian neural crest cell epithelial to mesenchymal transition and delamination

Epithelial to mesenchymal transition (EMT) is a cellular process that converts epithelial cells to mesenchymal cells with migratory potential in both developmental and pathological processes. Although originally considered a binary event, EMT in cancer progression involves intermediate states between a fully epithelial and a fully mesenchymal phenotype, which are characterized by distinct combinations of epithelial and mesenchymal markers. This phenomenon has been termed epithelial to mesenchymal plasticity (EMP), however, the intermediate states remain poorly described and it's unclear whether they exist during developmental EMT. Neural crest cells (NCC) are an embryonic progenitor cell population that gives rise to numerous cell types and tissues in vertebrates, and their formation is a classic example of developmental EMT. An important feature of NCC development is their delamination from the neuroepithelium via EMT, following which NCC migrate throughout the embryo and undergo differentiation. NCC delamination shares similar changes in cellular state and structure with cancer cell invasion. However, whether intermediate states also exist during NCC EMT and delamination remains unknown. Through single cell RNA sequencing, we identified intermediate NCC states based on their transcriptional signature and then spatially defined their locations in situ in the dorsolateral neuroepithelium. Our results illustrate the progressive transcriptional and spatial transitions from premigratory to migratory cranial NCC during EMT and delamination. Of note gene expression and trajectory analysis indicate that distinct intermediate populations of NCC delaminate in either S phase or G2/M phase of the cell cycle, and the importance of cell cycle regulation in facilitating mammalian cranial NCC delamination was confirmed through cell cycle inhibition studies. Additionally, transcriptional knockdown revealed a functional role for the intermediate stage marker Dlc1 in regulating NCC delamination and migration. Overall, our work identifying and characterizing the intermediate cellular states, processes, and molecular signals that regulate mammalian NCC EMT and delamination furthers our understanding of developmental EMP and may provide new insights into mechanisms regulating pathological EMP.

Glycosylation-related genes mediated prognostic signature contribute to prognostic prediction and treatment options in ovarian cancer: based on bulk and single‑cell RNA sequencing data

BACKGROUND

Ovarian cancer (OC) is a complex disease with significant tumor heterogeneity with the worst prognosis and highest mortality among all gynecological cancers. Glycosylation is a specific post-translational modification that plays an important role in tumor progression, immune escape and metastatic spread. The aim of this work was to identify the major glycosylation-related genes (GRGs) in OC and construct an effective GRGs signature to predict prognosis and immunotherapy.

METHODS

AUCell algorithm was used to identify glycosylation-related genes (GRGs) based on the scRNA-seq and bulk RNA-seq data. An effective GRGs signature was conducted using COX and LASSO regression algorithm. The texting dataset and clinical sample data were used to assessed the accuracy of GRGs signature. We evaluated the differences in immune cell infiltration, enrichment of immune checkpoints, immunotherapy response, and gene mutation status among different risk groups. Finally, RT-qPCR, Wound-healing assay, Transwell assay were performed to verify the effect of the CYBRD1 on OC.

RESULTS

A total of 1187 GRGs were obtained and a GRGs signature including 16 genes was established. The OC patients were divided into high- and low- risk group based on the median riskscore and the patients in high-risk group have poor outcome. We also found that the patients in low-risk group have higher immune cell infiltration, enrichment of immune checkpoints and immunotherapy response. The results of laboratory test showed that CYBRD1 can promote the invasion, and migration of OC and is closely related to the poor prognosis of OC patients.

CONCLUSIONS

Our study established a GRGs signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prognosis prediction and treatment strategy for OC.

Update on Epithelial-Mesenchymal Plasticity in Cancer Progression

Epithelial-mesenchymal transition (EMT) is a cellular process by which epithelial cells lose their characteristics and acquire mesenchymal traits to promote cell movement. This program is aberrantly activated in human cancers and endows tumor cells with increased abilities in tumor initiation, cell migration, invasion, metastasis, and therapy resistance. The EMT program in tumors is rarely binary and often leads to a series of gradual or intermediate epithelial-mesenchymal states. Functionally, epithelial-mesenchymal plasticity (EMP) improves the fitness of cancer cells during tumor progression and in response to therapies. Here, we discuss the most recent advances in our understanding of the diverse roles of EMP in tumor initiation, progression, metastasis, and therapy resistance and address major clinical challenges due to EMP-driven phenotypic heterogeneity in cancer. Uncovering novel molecular markers and key regulators of EMP in cancer will aid the development of new therapeutic strategies to prevent cancer recurrence and overcome therapy resistance.

Phenotyping EMT and MET cellular states in lung cancer patient liquid biopsies at a personalized level using mass cytometry

Malignant pleural effusions (MPEs) can be utilized as liquid biopsy for phenotyping malignant cells and for precision immunotherapy, yet MPEs are inadequately studied at the single-cell proteomic level. Here we leverage mass cytometry to interrogate immune and epithelial cellular profiles of primary tumors and pleural effusions (PEs) from early and late-stage non-small cell lung cancer (NSCLC) patients, with the goal of assessing epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in patient specimens. By using the EMT-MET reference map PHENOSTAMP, we observe a variety of EMT states in cytokeratin positive (CK+) cells, and report for the first time MET-enriched CK+ cells in MPEs. We show that these states may be relevant to disease stage and therapy response. Furthermore, we found that the fraction of CD33+ myeloid cells in PEs was positively correlated to the fraction of CK+ cells. Longitudinal analysis of MPEs drawn 2 months apart from a patient undergoing therapy, revealed that CK+ cells acquired heterogeneous EMT features during treatment. We present this work as a feasibility study that justifies deeper characterization of EMT and MET states in malignant cells found in PEs as a promising clinical platform to better evaluate disease progression and treatment response at a personalized level.

Mechanisms of Regulation of the Expression of miRNAs and lncRNAs by Metformin in Ovarian Cancer

Ovarian cancer (OC) is one of the most lethal gynecological malignancies. The use of biological compounds such as non-coding RNAs (ncRNAs) is being considered as a therapeutic option to improve or complement current treatments since the deregulation of ncRNAs has been implicated in the pathogenesis and progression of OC. Old drugs with antitumoral properties have also been studied in the context of cancer, although their antitumor mechanisms are not fully clear. For instance, the antidiabetic drug metformin has shown pleiotropic effects in several in vitro models of cancer, including OC. Interestingly, metformin has been reported to regulate ncRNAs, which could explain its diverse effects on tumor cells. In this review, we discuss the mechanism of epigenetic regulation described for metformin, with a focus on the evidence of metformin-dependent microRNA (miRNAs) and long non-coding RNA (lncRNAs) regulation in OC.

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.

Advances in Clinical Mass Cytometry

The advent of high-dimensional single-cell technologies has enabled detection of cellular heterogeneity and functional diversity of immune cells during health and disease conditions. Because of its multiplexing capabilities and limited compensation requirements, mass cytometry or cytometry by time of flight (CyTOF) has played a superior role in immune monitoring compared with flow cytometry. Further, it has higher throughput and lower cost compared with other single-cell techniques. Several published articles have utilized CyTOF to identify cellular phenotypes and features associated with disease outcomes. This article introduces CyTOF-based assays to profile immune cell-types, cell-states, and their applications in clinical research.

Role of epithelial-mesenchymal transition factor SNAI1 and its targets in ovarian cancer aggressiveness

Ovarian cancer remains the most lethal gynecologic malignancy in the USA. For over twenty years, epithelial-mesenchymal transition (EMT) has been characterized extensively in development and disease. The dysregulation of this process in cancer has been identified as a mechanism by which epithelial tumors become more aggressive, allowing them to survive and invade distant tissues. This occurs in part due to the increased expression of the EMT transcription factor, SNAI1 (Snail). In the case of epithelial ovarian cancer, Snail has been shown to contribute to cancer invasion, stemness, chemoresistance, and metabolic changes. Thus, in this review, we focus on summarizing current findings on the role of EMT (specifically, factors downstream of Snail) in determining ovarian cancer aggressiveness.

Tumor metabolism rewiring in epithelial ovarian cancer

The mortality rate of epithelial ovarian cancer (EOC) remains the first in malignant tumors of the female reproductive system. The characteristics of rapid proliferation, extensive implanted metastasis, and treatment resistance of cancer cells require an extensive metabolism rewiring during the progression of cancer development. EOC cells satisfy their rapid proliferation through the rewiring of perception, uptake, utilization, and regulation of glucose, lipids, and amino acids. Further, complete implanted metastasis by acquiring a superior advantage in microenvironment nutrients competing. Lastly, success evolves under the treatment stress of chemotherapy and targets therapy. Understanding the above metabolic characteristics of EOCs helps to find new methods of its treatment.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

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.

ROR1: an orphan becomes apparent

Since its initial identification in 1992 as a possible class 1 cell-surface receptor without a known parent ligand, receptor tyrosine kinase-like orphan receptor 1 (ROR1) has stimulated research, which has made apparent its significance in embryonic development and cancer. Chronic lymphocytic leukemia (CLL) was the first malignancy found to have distinctive expression of ROR1, which can help distinguish leukemia cells from most noncancer cells. Aside from its potential utility as a diagnostic marker or target for therapy, ROR1 also factors in the pathophysiology of CLL. This review is a report of the studies that have elucidated the expression, biology, and evolving strategies for targeting ROR1 that hold promise for improving the therapy of patients with CLL or other ROR1-expressing malignancies.

Hybrid Fluorescent Mass-Tag Nanotrackers as Universal Reagents for Long-Term Live-Cell Barcoding

Barcoding and pooling cells for processing as a composite sample are critical to minimize technical variability in multiplex technologies. Fluorescent cell barcoding has been established as a standard method for multiplexing in flow cytometry analysis. In parallel, mass-tag barcoding is routinely used to label cells for mass cytometry. Barcode reagents currently used label intracellular proteins in fixed and permeabilized cells and, therefore, are not suitable for studies with live cells in long-term culture prior to analysis. In this study, we report the development of fluorescent palladium-based hybrid-tag nanotrackers to barcode live cells for flow and mass cytometry dual-modal readout. We describe the preparation, physicochemical characterization, efficiency of cell internalization, and durability of these nanotrackers in live cells cultured over time. In addition, we demonstrate their compatibility with standardized cytometry reagents and protocols. Finally, we validated these nanotrackers for drug response assays during a long-term coculture experiment with two barcoded cell lines. This method represents a new and widely applicable advance for fluorescent and mass-tag barcoding that is independent of protein expression levels and can be used to label cells before long-term drug studies.

Measuring trogocytosis between ovarian tumor and natural killer cells

Trogocytosis is an active transport mechanism by which one cell extracts a plasma membrane fragment with embedded molecules from an adjacent cell in a contact-dependent process leading to the acquisition of a new function. Our protocol, which has general applicability, consolidates and optimizes existing protocols while highlighting key experimental variables to demonstrate that natural killer (NK) cells acquire the tetraspanin CD9 by trogocytosis from ovarian tumor cells. For complete details on the use and execution of this protocol, please refer to Gonzalez et al. (2021).

CyTOF® for the Masses

Mass cytometry has revolutionized immunophenotyping, particularly in exploratory settings where simultaneous breadth and depth of characterization of immune populations is needed with limited samples such as in preclinical and clinical tumor immunotherapy. Mass cytometry is also a powerful tool for single-cell immunological assays, especially for complex and simultaneous characterization of diverse intratumoral immune subsets or immunotherapeutic cell populations. Through the elimination of spectral overlap seen in optical flow cytometry by replacement of fluorescent labels with metal isotopes, mass cytometry allows, on average, robust analysis of 60 individual parameters simultaneously. This is, however, associated with significantly increased complexity in the design, execution, and interpretation of mass cytometry experiments. To address the key pitfalls associated with the fragmentation, complexity, and analysis of data in mass cytometry for immunologists who are novices to these techniques, we have developed a comprehensive resource guide. Included in this review are experiment and panel design, antibody conjugations, sample staining, sample acquisition, and data pre-processing and analysis. Where feasible multiple resources for the same process are compared, allowing researchers experienced in flow cytometry but with minimal mass cytometry expertise to develop a data-driven and streamlined project workflow. It is our hope that this manuscript will prove a useful resource for both beginning and advanced users of mass cytometry.

Epithelial to mesenchymal transition during mammalian neural crest cell delamination

Epithelial to mesenchymal transition (EMT) is a well-defined cellular process that was discovered in chicken embryos and described as "epithelial to mesenchymal transformation" [1]. During EMT, epithelial cells lose their epithelial features and acquire mesenchymal character with migratory potential. EMT has subsequently been shown to be essential for both developmental and pathological processes including embryo morphogenesis, wound healing, tissue fibrosis and cancer [2]. During the past 5 years, interest and study of EMT especially in cancer biology have increased exponentially due to the implied role of EMT in multiple aspects of malignancy such as cell invasion, survival, stemness, metastasis, therapeutic resistance and tumor heterogeneity [3]. Since the process of EMT in embryogenesis and cancer progression shares similar phenotypic changes, core transcription factors and molecular mechanisms, it has been proposed that the initiation and development of carcinoma could be attributed to abnormal activation of EMT factors usually required for normal embryo development. Therefore, developmental EMT mechanisms, whose timing, location, and tissue origin are strictly regulated, could prove useful for uncovering new insights into the phenotypic changes and corresponding gene regulatory control of EMT under pathological conditions. In this review, we initially provide an overview of the phenotypic and molecular mechanisms involved in EMT and discuss the newly emerging concept of epithelial to mesenchymal plasticity (EMP). Then we focus on our current knowledge of a classic developmental EMT event, neural crest cell (NCC) delamination, highlighting key differences in our understanding of NCC EMT between mammalian and non-mammalian species. Lastly, we highlight available tools and future directions to advance our understanding of mammalian NCC EMT.

Targeting and neutralizing human epididymis protein 4 by novel nanobodies to suppress ovarian cancer cells and attenuate cisplatin resistance

Human epididymis protein 4 (HE4) is a glycoprotein secreted by epithelial ovarian cancer (EOC) cells and is a novel and specific biomarker for diagnosing and prognosing EOC. Previous studies have shown that overexpression of HE4 is correlated with EOC tumorigenesis and chemoresistance. However, less has been reported regarding the direct effect of the secreted HE4 protein as an autocrine factor in EOC cells. Here, we investigated the molecular mechanism of the secretory form of HE4 on the growth of EOC cells by applying nanobodies with a targeted interaction of free HE4. Three anti-HE4 nanobodies were selected from an immune library by phage display. HE4 secreted by serum-free cultured OVCAR3 cells increased and was effectively neutralized by anti-HE4 nanobodies, which inhibited cell viability. Treatment with the anti-HE4 nanobody 1G8 decreased Bcl-2 expression and increased BAX, cleaved PARP, and p53 levels, resulting in apoptosis of OVCAR3 cells. Moreover, 1G8 significantly improved the cisplatin response of OVCAR3 cells. Our data suggest that secretory HE4 played a novel pro-survival autocrine role and was a target of the anti-HE4 nanobody to improve the therapeutic effects of cisplatin-based chemotherapy.

Circulating tumor cells as a "real-time liquid biopsy": Recent advances and the application in ovarian cancer

Even with the latest advances in technology, the treatment of ovarian cancer remains a big challenge because it is typically diagnosed at advanced stage, is prone to early relapse in spite of aggressive treatment and has an extremely poor prognosis. Circulating tumor cells (CTCs) can be used as a non-invasive "real-time liquid biopsy", which has shown the value of diagnosis, assessment of prognosis and chemoresistance, and detection of small residual tumors on ovarian cancer. This review article provides an overview on recent research on CTCs in ovarian cancer, with special focus on the clinical application of CTC tests.

Mass Cytometry for the Characterization of Individual Cell Types in Ovarian Solid Tumors

Mass cytometry aka Cytometry by Time-Of-Flight (CyTOF) is one of several recently developed multiparametric single-cell technologies designed to address cellular heterogeneity within healthy and diseased tissue. Mass cytometry is an adaptation of flow cytometry in which antibodies are labeled with stable heavy metal isotopes and the readout is by time-of-flight mass spectrometry. With minimal spillover between channels, mass cytometry enables readouts of up to 60 parameters per single cell. Critically, mass cytometry can identify minority cell populations that are lost in bulk tissue analysis. Mass cytometry has been used to great effect for the study of immune cells. We have extended its use to examine single cells within disaggregated solid tissues, specifically freshly resected tubo-ovarian high-grade serous tumors. Here we detail our protocols designed to ensure the production of high-quality single-cell datasets. The methodology can be modified to accommodate the study of other solid tissues.

Assessment of Tumor Heterogeneity in High-Grade Serous Ovarian Cancer: Mass Cytometry to Understand the Complex Tumor Biology

Ovarian cancer (OC) is the most deadly gynecological malignancy worldwide. OC patients undergo debulking surgery followed by platinum/taxane-based chemotherapy; however, despite recent development of new therapeutic approaches based on combination of chemotherapy and innovative targeted-therapies, most of them relapse due to chemoresistance. Many studies have been carried out to decipher the high heterogeneity of ovarian cancer cells that drives tumor treatment failure. Here, we describe our experience in the characterization of ovarian cancer cell subsets through a high-resolution technology in multiparametric analysis, such as mass cytometry (MC).

Analysis of the Single-Cell Heterogeneity of Adenocarcinoma Cell Lines and the Investigation of Intratumor Heterogeneity Reveals the Expression of Transmembrane Protein 45A (TMEM45A) in Lung Adenocarcinoma Cancer Patients

Simple Summary

Non-small cell lung cancer (NSCLC) is one of the main causes of cancer-related deaths worldwide. Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). Human NSCLC adenocarcinoma cells A549, H1975, and H1650 were studied at single-cell resolution for the expression pattern of 13 markers: GLUT1, MCT4, CA9, TMEM45A, CD66, CD274, CD24, CD326, pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The intra- and inter-cell-line heterogeneity of A549, H1975, and H1650 cells were demonstrated through hypoxic modeling. Additionally, human primary lung adenocarcinoma, and non-involved healthy lung tissue were homogenized to prepare a single-cell suspension for CyTOF analysis. The single-cell heterogeneity was confirmed using unsupervised viSNE and FlowSOM analysis. Our results also show, for the first time, that TMEM45A is expressed in lung adenocarcinoma.

Abstract

Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). First, we studied the single-cell heterogeneity of frequent NSCLC adenocarcinoma models, such as A549, H1975, and H1650. The intra- and inter-cell-line single-cell heterogeneity is represented in the expression patterns of 13 markers—namely GLUT1, MCT4, CA9, TMEM45A, CD66, CD274 (PD-L1), CD24, CD326 (EpCAM), pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The qRT-PCR and CyTOF analyses revealed that a hypoxic microenvironment and altered metabolism may influence cell-line heterogeneity. Additionally, human primary lung adenocarcinoma and non-involved healthy lung tissue biopsies were homogenized to prepare a single-cell suspension for CyTOF analysis. The CyTOF showed the ITH of human primary lung adenocarcinoma for 14 markers; particularly, the higher expressions of GLUT1, MCT4, CA9, TMEM45A, and CD66 were associated with the lung-tumor tissue. Our single-cell results are the first to demonstrate TMEM45A expression in human lung adenocarcinoma, which was verified by immunohistochemistry.

Spatial mapping of cancer tissues by OMICS technologies

Spatial mapping of heterogeneity in gene expression in cancer tissues can improve our understanding of cancers and help in the rapid detection of cancers with high accuracy and reliability. Significant advancements have been made in recent years in OMICS technologies, which possess the strong potential to be applied in the spatial mapping of biopsy tissue samples and their molecular profiling to a single-cell level. The clinical application of OMICS technologies in spatial profiling of cancer tissues is also advancing. The current review presents recent advancements and prospects of applying OMICS technologies to the spatial mapping of various analytes in cancer tissues. We benchmark the current state of the art in the field to advance existing OMICS technologies for high throughput spatial profiling. The factors taken into consideration include spatial resolution, types of biomolecules, number of different biomolecules that can be detected from the same assay, labeled versus label-free approaches, and approximate time required for each assay. Further advancements are still needed for the widespread application of OMICs technologies in performing fast and high throughput spatial mapping of cancer tissues as well as their effective use in research and clinical applications.

Immunological configuration of ovarian carcinoma: features and impact on disease outcome

Epithelial ovarian carcinoma (EOC) is a relatively rare malignancy but is the fifth-leading cause of cancer-related death in women, largely reflecting early, prediagnosis dissemination of malignant disease to the peritoneum. At odds with other neoplasms, EOC is virtually insensitive to immune checkpoint inhibitors, correlating with a tumor microenvironment that exhibits poor infiltration by immune cells and active immunosuppression. Here, we comparatively summarize the humoral and cellular features of primary and metastatic EOC, comparatively analyze their impact on disease outcome, and propose measures to alter them in support of treatment sensitivity and superior patient survival.

High-grade serous ovarian tumor cells modulate NK cell function to create an immune-tolerant microenvironment

Tubo-ovarian high-grade serous carcinoma (HGSC) is unresponsive to immune checkpoint blockade despite significant frequencies of exhausted T cells. Here we apply mass cytometry and uncover decidual-like natural killer (dl-NK) cell subpopulations (CD56+CD9+CXCR3+KIR+CD3-CD16-) in newly diagnosed HGSC samples that correlate with both tumor and transitioning epithelial-mesenchymal cell abundance. We show different combinatorial expression patterns of ligands for activating and inhibitory NK receptors within three HGSC tumor compartments: epithelial (E), transitioning epithelial-mesenchymal (EV), and mesenchymal (vimentin expressing [V]), with a more inhibitory ligand phenotype in V cells. In cocultures, NK-92 natural killer cells acquire CD9 from HGSC tumor cells by trogocytosis, resulting in reduced anti-tumor cytokine production and cytotoxicity. Cytotoxicity in these cocultures is restored with a CD9-blocking antibody or CD9 CRISPR knockout, thereby identifying mechanisms of immune suppression in HGSC. CD9 is widely expressed in HGSC tumors and so represents an important new therapeutic target with immediate relevance for NK immunotherapy.

Partial EMT in head and neck cancer biology: a spectrum instead of a switch

Our understanding of epithelial-to-mesenchymal transition (EMT) has slowly evolved from a simple two state, binary model to a multi-step, dynamic continuum of epithelial-to-mesenchymal plasticity, with metastable intermediate transition states that may drive cancer metastasis. Head and neck cancer is no exception, and in this review, we use head and neck as a case study for how partial-EMT (p-EMT) cell states may play an important role in cancer progression. In particular, we summarize recent in vitro and in vivo studies that uncover these intermediate transition states, which exhibit both epithelial and mesenchymal properties and appear to have distinct advantages in migration, survival in the bloodstream, and seeding and propagation within secondary metastatic sites. We then summarize the common and distinct regulators of p-EMT as well as methodologies for identifying this unique cellular subpopulation, with a specific emphasis on the role of cutting-edge technologies, such as single cell approaches. Finally, we propose strategies to target p-EMT cells, highlighting potential opportunities for therapeutic intervention to specifically target the process of metastasis. Thus, although significant challenges remain, including numerous gaps in current knowledge, a deeper understanding of EMT plasticity and a genuine identification of EMT as spectrum rather than a switch will be critical for improving patient diagnosis and treatment across oncology.

Training Novices in Generation and Analysis of High-Dimensional Human Cell Phospho-Flow Cytometry Data

This article presents a single experiment designed to introduce a trainee to multiple advanced bench and analysis techniques, including high-dimensional cytometry, profiling cell signaling networks, functional assays with primary human tissue, and single-cell analysis with machine learning tools. The trainee is expected to have only minimal laboratory experience and is not required to have any prior training in flow cytometry, immunology, or data science. This article aims to introduce the advanced research areas with a design that is robust enough that novice trainees will succeed, flexible enough to allow some project customization, and fundamental enough that the skills and knowledge gained will provide a template for future experiments. For advanced users, the updated phospho-flow protocol and the established controls, best practices, and expected outcomes presented here also provide a framework for adapting these tools in new areas with unexplored biology. © 2020 by John Wiley & Sons, Inc. Basic Protocol: Phospho-protein stimulation and mass cytometry data collection Support Protocol: Analysis of signaling mass cytometry data.

Predicting ROR1/BCL2 combination targeted therapy of small cell carcinoma of the lung

Small cell lung cancer (SCLC) remains a deadly form of cancer, with a 5-year survival rate of less than 10 percent, necessitating novel therapies. Receptor tyrosine kinase-like orphan receptor 1 (ROR1) is an oncofetal protein that is emerging as a therapeutic target and is co-expressed with BCL2 in multiple tumor types due to microRNA coregulation. We hypothesize that ROR1-targeted therapy is effective in small cell lung cancer and synergizes with therapeutic BCL2 inhibition. Tissue microarrays (TMAs) and formalin-fixed paraffin-embedded (FFPE) SCLC patient samples were utilized to determine the prevalence of ROR1 and BCL2 expression in SCLC. Eight SCLC-derived cell lines were used to determine the antitumor activity of a small molecule ROR1 inhibitor (KAN0441571C) alone and in combination with the BCL2 inhibitor venetoclax. The Chou-Talalay method was utilized to determine synergy with the drug combination. ROR1 and BCL2 protein expression was identified in 93% (52/56) and 86% (48/56) of SCLC patient samples, respectively. Similarly, ROR1 and BCL2 were shown by qRT-PCR to have elevated expression in 79% (22/28) and 100% (28/28) of SCLC patient samples, respectively. KAN0441571C displayed efficacy in 8 SCLC cell lines, with an IC50 of 500 nM or less. Synergy as defined by a combination index of <1 via the Chou-Talalay method between KAN0441571C and venetoclax was demonstrated in 8 SCLC cell lines. We have shown that ROR1 inhibition is synergistic with BCL2 inhibition in SCLC models and shows promise as a novel therapeutic target in SCLC.

Measuring and Modelling the Epithelial- Mesenchymal Hybrid State in Cancer: Clinical Implications

The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques.

An Automatic Platform Based on Nanostructured Microfluidic Chip for Isolating and Identification of Circulating Tumor Cells

Circulating tumor cell (CTC) test is currently used as a biomarker in cancer treatment. Unfortunately, the poor reproducibility and limited sensitivity with the CTC detection have limited its potential impact on clinical application. A reliable automated CTC detection system is therefore needed. We have designed an automated microfluidic chip-based CTC detection system and hypothesize this novel system can reliably detect CTC from clinical specimens. SKOV3 ovarian cancer cell line was used first to test the reliability of our system. Ten healthy volunteers, 5 patients with benign ovarian tumors, and 8 patients with epithelial ovarian cancer (EOC) were recruited to validate the CTC capturing efficacy in the peripheral blood. The capture rates for spiking test in SKOV3 cells were 48.3% and 89.6% by using anti-EpCAM antibody alone and a combination of anti-EpCAM antibody and anti-N-cadherin antibody, respectively. The system was sensitive to detection of low cell count and showed a linear relationship with the cell counts in our test range. The sensitivity and specificity were 62.5% and 100% when CTC was used as a biomarker for EOC. Our results demonstrated that this automatic CTC platform has a high capture rate and is feasible for detection of CTCs in EOC.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.

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.

Emerging Concepts of Hybrid Epithelial-to-Mesenchymal Transition in Cancer Progression

Epithelial mesenchymal transition (EMT) is a complex process through which epithelial (E) cells lose their adherens junctions, transform into mesenchymal (M) cells and attain motility, leading to metastasis at distant organs. Nowadays, the concept of EMT has shifted from a binary phase of interconversion of pure E to M cells and vice versa to a spectrum of E/M transition states preferably coined as hybrid/partial/intermediate EMT. Hybrid EMT, being a plastic transient state, harbours cells which co-express both E and M markers and exhibit high tumourigenic properties, leading to stemness, metastasis, and therapy resistance. Several preclinical and clinical studies provided the evidence of co-existence of E/M phenotypes. Regulators including transcription factors, epigenetic regulators and phenotypic stability factors (PSFs) help in maintaining the hybrid state. Computational and bioinformatics approaches may be excellent for identifying new factors or combinations of regulatory elements that govern the different EMT transition states. Therapeutic intervention against hybrid E/M cells, though few, may evolve as a rational strategy against metastasis and drug resistance. This review has attempted to present the recent advancements on the concept and regulation of the process of hybrid EMT which generates hybrid E/M phenotypes, evidence of intermediate EMT in both preclinical and clinical setup, impact of partial EMT on promoting tumourigenesis, and future strategies which might be adapted to tackle this phenomenon.

Progress and applications of mass cytometry in sketching immune landscapes

Recently emerged mass cytometry (cytometry by time-of-flight [CyTOF]) technology permits the identification and quantification of inherently diverse cellular systems, and the simultaneous measurement of functional attributes at the single-cell resolution. By virtue of its multiplex ability with limited need for compensation, CyTOF has led a critical role in immunological research fields. Here, we present an overview of CyTOF, including the introduction of CyTOF principle and advantages that make it a standalone tool in deciphering immune mysteries. We then discuss the functional assays, introduce the bioinformatics to interpret the data yield via CyTOF, and depict the emerging clinical and research applications of CyTOF technology in sketching immune landscape in a wide variety of diseases.

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.

The Immune Profile of Pituitary Adenomas and a Novel Immune Classification for Predicting Immunotherapy Responsiveness

CONTEXT

The tumor immune microenvironment is associated with clinical outcomes and immunotherapy responsiveness.

OBJECTIVE

To investigate the intratumoral immune profile of pituitary adenomas (PAs) and its clinical relevance and to explore a novel immune classification for predicting immunotherapy responsiveness.

DESIGN, PATIENTS, AND METHODS

The transcriptomic data from 259 PAs and 20 normal pituitaries were included for analysis. The ImmuCellAI algorithm was used to estimate the abundance of 24 types of tumor-infiltrating immune cells (TIICs) and the expression of immune checkpoint molecules (ICMs).

RESULTS

The distributions of TIICs differed between PAs and normal pituitaries and varied among PA subtypes. T cells dominated the immune microenvironment across all subtypes of PAs. The tumor size and patient age were correlated with the TIIC abundance, and the ubiquitin-specific protease 8 (USP8) mutation in corticotroph adenomas influenced the intratumoral TIIC distributions. Three immune clusters were identified across PAs based on the TIIC distributions. Each cluster of PAs showed unique features of ICM expression that were correlated with distinct pathways related to tumor development and progression. CTLA4/CD86 expression was upregulated in cluster 1, whereas programmed cell death protein 1/programmed cell death 1 ligand 2 (PD1/PD-L2) expression was upregulated in cluster 2. Clusters 1 and 2 exhibited a "hot" immune microenvironment and were predicted to exhibit higher immunotherapy responsiveness than cluster 3, which exhibited an overall "cold" immune microenvironment.

CONCLUSIONS

We summarized the immune profile of PAs and identified 3 novel immune clusters. These findings establish a foundation for further immune studies on PAs and provide new insights into immunotherapy strategies for PAs.

Epithelial-mesenchymal plasticity: emerging parallels between tissue morphogenesis and cancer metastasis

Many cells possess epithelial–mesenchymal plasticity (EMP), which allows them to shift reversibly between adherent, static and more detached, migratory states. These changes in cell behaviour are driven by the programmes of epithelial–mesenchymal transition (EMT) and mesenchymal–epithelial transition (MET), both of which play vital roles during normal development and tissue homeostasis. However, the aberrant activation of these processes can also drive distinct stages of cancer progression, including tumour invasiveness, cell dissemination and metastatic colonization and outgrowth. This review examines emerging common themes underlying EMP during tissue morphogenesis and malignant progression, such as the context dependence of EMT transcription factors, a central role for partial EMTs and the nonlinear relationship between EMT and MET.

This article is part of a discussion meeting issue ‘Contemporary morphogenesis'.

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 Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry

The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning-based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician-scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics. © 2020 International Society for Advancement of Cytometry.

Unraveling the Complexity of the Cancer Microenvironment With Multidimensional Genomic and Cytometric Technologies

Cancers are characterized by extensive heterogeneity that occurs intratumorally, between lesions, and across patients. To study cancer as a complex biological system, multidimensional analyses of the tumor microenvironment are paramount. Single-cell technologies such as flow cytometry, mass cytometry, or single-cell RNA-sequencing have revolutionized our ability to characterize individual cells in great detail and, with that, shed light on the complexity of cancer microenvironments. However, a key limitation of these single-cell technologies is the lack of information on spatial context and multicellular interactions. Investigating spatial contexts of cells requires the incorporation of tissue-based techniques such as multiparameter immunofluorescence, imaging mass cytometry, or in situ detection of transcripts. In this Review, we describe the rise of multidimensional single-cell technologies and provide an overview of their strengths and weaknesses. In addition, we discuss the integration of transcriptomic, genomic, epigenomic, proteomic, and spatially-resolved data in the context of human cancers. Lastly, we will deliberate on how the integration of multi-omics data will help to shed light on the complex role of cell types present within the human tumor microenvironment, and how such system-wide approaches may pave the way toward more effective therapies for the treatment of cancer.

Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival. With a pilot mass cytometry dataset of 2 million cells from 28 glioblastomas, RAPID identified tumor cells whose abundance independently and continuously stratified patient survival. Statistical validation within the workflow included repeated runs of stochastic steps and cell subsampling. Biological validation used an orthogonal platform, immunohistochemistry, and a larger cohort of 73 glioblastoma patients to confirm the findings from the pilot cohort. RAPID was also validated to find known risk stratifying cells and features using published data from blood cancer. Thus, RAPID provides an automated, unsupervised approach for finding statistically and biologically significant cells using cytometry data from patient samples.

Advances of single-cell genomics and epigenomics in human disease: where are we now?

Cellular heterogeneity is revolutionizing the way to study, monitor and dissect complex diseases. This has been possible with the technological and computational advances associated to single-cell genomics and epigenomics. Deeper understanding of cell-to-cell variation and its impact on tissue function will open new avenues for early disease detection, accurate diagnosis and personalized treatments, all together leading to the next generation of health care. This review focuses on the recent discoveries that single-cell genomics and epigenomics have facilitated in the context of human health. It highlights the potential of single-cell omics to further advance the development of personalized treatments and precision medicine in cancer, diabetes and chronic age-related diseases. The promise of single-cell technologies to generate new insights about the differences in function between individual cells is just emerging, and it is paving the way for identifying biomarkers and novel therapeutic targets to tackle age, complex diseases and understand the effect of life style interventions and environmental factors.

A Novel Approach for Quantifying Cancer Cells Showing Hybrid Epithelial/Mesenchymal States in Large Series of Tissue Samples: Towards a New Prognostic Marker

In cancer biology, epithelial-to-mesenchymal transition (EMT) is associated with tumorigenesis, stemness, invasion, metastasis, and resistance to therapy. Evidence of co-expression of epithelial and mesenchymal markers suggests that EMT should be a stepwise process with distinct intermediate states rather than a binary switch. In the present study, we propose a morphological approach that enables the detection and quantification of cancer cells with hybrid E/M states, i.e., which combine partially epithelial (E) and partially mesenchymal (M) states. This approach is based on a sequential immunohistochemistry technique performed on the same tissue section, the digitization of whole slides, and image processing. The aim is to extract quantitative indicators able to quantify the presence of hybrid E/M states in large series of human cancer samples and to analyze their relationship with cancer aggressiveness. As a proof of concept, we applied our methodology to a series of about a hundred urothelial carcinomas and demonstrated that the presence of cancer cells with hybrid E/M phenotypes at the time of diagnosis is strongly associated with a poor prognostic value, independently of standard clinicopathological features. Although validation on a larger case series and other cancer types is required, our data support the hybrid E/M score as a promising prognostic biomarker for carcinoma patients.

LRRC4 Suppresses E-Cadherin-Dependent Collective Cell Invasion and Metastasis in Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is the most malignant gynecological carcinoma and is of a high incidence of death due to detection at late stages when metastasis already occurs. However, the mechanism underlying metastasis of EOC remains unclear. Analysis of the open database and experiments with immunochemistry showed that LRRC4 is lowly expressed in high-grade serous ovarian cancer (HGSC) cells and during EOC metastasis. The 3D cell culture system and the orthotopic ovarian xenograft model infected with LRRC4-containing adeno-associated virus serotype 9 (AAV9) were used to confirm collective invasion and metastasis of cells in vitro and in vivo. Phos-tag SDS-PAGE was used to detect the phosphorylation of LRRC4 and PIK3R1. A number of experiments with methods such as co-immunoprecipitation and immunoblotting were performed to explore the mechanism for the actions of LRRC4 and PIK3R1 in EOC metastasis. An inverse correlation between LRRC4 and E-cadherin expression was detected in the regions of invasion in primary EOC tissues and metastatic ascites. LRRC4 binds to the cSH2 domain of PIK3R1 and inhibits the activity of PIK3R1, without disrupting the physical interactions between PIK3R1 and PIK3CA. LRRC4 inhibits EOC metastasis by targeting E-cadherin-dependent collective cell invasion and does so by inhibiting the PIK3R1-mediated AKT/GSK3β/β-catenin signaling pathway. LRRC4 functions as a tumor suppressor gene to inhibit EOC collective invasion and metastasis in vitro and in vivo and does so by directly binding to the cSH2 domain of PIK3R1 to exert its regulatory function. Our findings provide a potential novel approach for metastasis prognosis and a new strategy for the treatment of EOC.

Advances in the Characterization of Circulating Tumor Cells in Metastatic Breast Cancer: Single Cell Analyses and Interactions, and Patient-Derived Models for Drug Testing

Metastasis is the major cause of breast cancer death worldwide. In metastatic breast cancer, circulating tumor cells (CTCs) can be captured from patient blood samples sequentially over time and thereby serve as surrogates to assess the biology of surviving cancer cells that may still persist in solitary or multiple metastatic sites following treatment. CTCs may thus function as potential real-time decision-making guides for selecting appropriate therapies during the course of disease or for the development and testing of new treatments. The heterogeneous nature of CTCs warrants the use of single cell platforms to better inform our understanding of these cancer cells. Current techniques for single cell analyses and techniques for investigating interactions between cancer and immune cells are discussed. In addition, methodologies for growing patient-derived CTCs in vitro or propagating them in vivo to facilitate CTC drug testing are reviewed. We advocate the use of CTCs in appropriate microenvironments to appraise the effectiveness of cancer chemotherapies, immunotherapies, and for the development of new cancer treatments, fundamental to personalizing and improving the clinical management of metastatic breast cancer.

Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution

Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies.

Intermediate transitions between epithelial and mesenchymal states are associated with tumor progression. Here using mass cytometry, Plevritis and colleagues develop a computational framework to resolve and map these trajectories in lung cancer cells and clinical specimens.