Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of complex tissues both in health and in disease. Over the past decade, scRNA-seq has been applied to tumour samples obtained from patients with cancer in hundreds of studies, thereby advancing the view that each tumour is a complex ecosystem and uncovering the diverse states of both cancer cells and the tumour microenvironment. Such studies have primarily investigated and provided insights into the basic biology of cancer, although considerable research interest exists in leveraging these findings towards clinical applications. In this Review, we summarize the available data from scRNA-seq studies investigating samples from patients with cancer with a particular focus on findings that are of potential clinical relevance. We highlight four main research objectives of scRNA-seq studies and describe some of the most relevant findings towards such goals. We also describe the limitations of scRNA-seq, as well as future approaches in this field that are anticipated to further advance clinical applicability.

MicroRNAs are released from cells in extracellular vesicles (EVs), representing an essential mode of cell-cell communication (CCC) via a regulatory effect on gene expression. Single-cell RNA-sequencing technologies have ushered in an era of elucidating CCC at single-cell resolution. Herein, we present miRTalk, a pioneering approach for inferring CCC mediated by EV-derived miRNA-target interactions (MiTIs). The benchmarking against simulated and real-world datasets demonstrates the superior performance of miRTalk, and the application to four disease scenarios reveals the in-depth MiTI-mediated CCC mechanisms. Collectively, miRTalk can infer EV-derived MiTI-mediated CCC with scRNA-seq data, providing new insights into the intercellular dynamics of biological processes.

Ovarian cancer is the most lethal gynaecological cancer and treatment options remain limited. In a recent first-in-class Phase I trial, the monoclonal IgE antibody MOv18, specific for the tumour-associated antigen Folate Receptor-α, was well-tolerated and preliminary anti-tumoural activity observed. Pre-clinical studies identified macrophages as mediators of tumour restriction and pro-inflammatory activation by IgE. However, the mechanisms of IgE-mediated modulation of macrophages and downstream tumour immunity in human cancer remain unclear. Here we study macrophages from patients with epithelial ovarian cancers naive to IgE therapy. High-dimensional flow cytometry and RNA-seq demonstrate immunosuppressive, FcεR-expressing macrophage phenotypes. Ex vivo co-cultures and RNA-seq interaction analyses reveal immunosuppressive associations between patient-derived macrophages and regulatory T (Treg) cells. MOv18 IgE-engaged patient-derived macrophages undergo pro-inflammatory repolarisation ex vivo and display induction of a hyperinflammatory, T cell-stimulatory subset. IgE reverses macrophage-promoted Treg cell induction to increase CD8+ T cell expansion, a signature associated with improved patient prognosis. On-treatment tumours from the MOv18 IgE Phase I trial show evidence of this IgE-driven immune signature, with increased CD68+ and CD3+ cell infiltration. We demonstrate that IgE induces hyperinflammatory repolarised states of patient-derived macrophages to inhibit Treg cell immunosuppression. These processes may collectively promote immune activation in ovarian cancer patients receiving IgE therapy.

Ovarian cancer is one of the most prevalent gynaecological malignancies. The rapid development of single-cell RNA sequencing (scRNA-seq) has allowed scientists to use this technique to study ovarian cancer development, heterogeneity, and tumour environment. Although multiple original research articles have reported the use of scRNA-seq in understanding ovarian cancer and how therapy resistance occurs, there is a lack of a comprehensive review that could summarize the findings from multiple studies. Therefore, this review aimed to fill this gap by comparing and summarizing the results from different studies that have used scRNA-seq in understanding ovarian cancer development, heterogeneity, tumour microenvironment, and treatment resistance. This review will begin with an overview of scRNA-seq workflow, followed by a discussion of various applications of scRNA-seq in studying ovarian cancer. Next, the limitations and future directions of scRNA-seq in ovarian cancer research will be presented.

To investigate the impact of the tumor microenvironment (TME) on the responsiveness to chemotherapy in ovarian cancer (OV).

We integrated single cell RNA-seq datasets of OV containing chemo-response information, and characterize their clusters based on different TME sections. We focus on analyzing cell-cell communication to elaborate on the mechanisms by which different components of the TME directly influence the chemo-response of tumor cells.

scRNA-seq datasets were annotated according to specific markers for different cell types. Differential analysis of malignant epithelial cells revealed that chemoresistance was associated with the TME. Notably, distinct TME components exhibited varying effects on chemoresistance. Enriched SPP1+ tumor-associated macrophages in chemo-resistant patients could promote chemoresistance through SPP1 binding to CD44 on tumor cells. Additionally, the overexpression of THBS2 in stromal cells could promote chemoresistance through binding with CD47 on tumor cells. In contrast, GZMA in the lymphocytes could downregulate the expression of PARD3 through direct interaction with PARD3, thereby attenuating chemoresistance in tumor cells.

Our study indicates that the non-tumor cell components of the TME (e.g. SPP1+ TAMs, stromal cells and lymphocytes) can directly impact the chemo-response of OV and targeting the TME was potentially crucial in chemotherapy of OV.

Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution cell spatial profiles consisting of cell types, cell states and their spatial network from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators, including prognosis and drug response biomarkers, across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest, and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.

Patients with high-grade serous ovarian carcinoma (HGSOC) are virtually insensitive to immune checkpoint inhibitors (ICI) employed as standalone therapeutics, at least in part reflecting microenvironmental immunosuppression. Thus, conventional chemotherapeutics and targeted anticancer agents that not only mediate cytotoxic effects but also promote the recruitment of immune effector cells to the HGSOC microenvironment stand out as promising combinatorial partners for ICIs in this oncological indication.

We harnessed a variety of transcriptomic, spatial, and functional assays to characterize the differential impact of neoadjuvant paclitaxel-carboplatin on the immunological configuration of paired primary and metastatic HGSOC biopsies as compared to neoadjuvant chemotherapy (NACT)-naïve HGSOC samples from five independent patient cohorts.

We found NACT-driven endoplasmic reticulum stress and calreticulin exposure in metastatic HGSOC lesions culminates with the establishment of a dense immune infiltrate including follicular T cells (TFH cells), a prerequisite for mature tertiary lymphoid structure (TLS) formation. In this context, TLS maturation was associated with an increased intratumoral density of ICI-sensitive TCF1+PD1+ CD8+ T cells over their ICI-insensitive TIM-3+PD1+ counterparts. Consistent with this notion, chemotherapy coupled with a PD1-targeting ICI provided a significant survival benefit over either therapeutic approach in syngeneic models of HGSOC bearing high (but not low) tumor mutational burden.

Altogether, our findings suggest that NACT promotes TLS formation and maturation in HGSOC lesions, de facto preserving an intratumoral ICI-sensitive T-cell phenotype. These observations emphasize the role of rational design, especially relative to the administration schedule, for clinical trials testing chemotherapy plus ICIs in patients with HGSOC. See related commentary by Bravo Melgar and Laoui, p. 10.

The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multicell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell-type-specific spatial organization. To this end, we developed Spatial Deconvolution Explorer (SpatialDeX), a regression model-based method for estimating cell-type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring single-cell RNA-seq references. Significance: The development of a reference-free method for deconvolving the identity of cells in spatial transcriptomics datasets enables exploration of tumor architecture to gain deeper insights into the dynamics of the tumor microenvironment.

Single-cell RNA sequencing (scRNAseq) of tumour-infiltrating immune cells in high-grade serous ovarian cancer (HGSOC) omental biopsies reveals potential targets that could enhance response to neo-adjuvant chemotherapy (NACT). Analysis of 64,097 cells identifies NACT-induced overexpression of stabilin-1 (clever-1) on macrophages and FOXP3 in Tregs that is confirmed at the protein level. STAB1 inhibition in vitro induces anti-tumour macrophages. FOXP3 anti-sense oligonucleotide (FOXP3-ASO), repolarises Tregs to an effector T cell phenotype. ScRNAseq on 69,781 cells from an HGSOC syngeneic mouse model recapitulates the patients' data. Combining chemotherapy with anti-stabilin1 antibody and/or Foxp3-ASO significantly increases survival of mice with established peritoneal disease in two HGSOC syngeneic models and progression-free survival in a third model. Long-term survivors (300 days + ) are resistant to tumour rechallenge. Anti-stabilin1 antibody enriches the tumours with CXCL9+ macrophages and Foxp3-ASO increases TBET cell infiltration. Our results suggest that targeting these molecules in immune cells may improve chemotherapy response in patients.

Our study aims to explore the effects of neoadjuvant chemotherapy (NACT) on tumour cells and immune cells in the immune microenvironment of patients with high-grade serous ovarian cancer (HGSOC). Single-cell RNA sequencing data of paired ovarian cancer tissues were analysed before and after NACT in 11 patients with HGSOC. The effect of NACT on two major cell components of the tumour microenvironment, epithelial cells and CD8+T cells, was investigated. The mechanisms of epithelial cell evasion by NACT and immune killing were explored from the perspectives of gene expression, functional characteristics, transcriptional regulation, and cell communication. Key targets for reversing NACT resistance were identified and possible therapeutic strategies proposed. While NACT improved the de novo differentiation of anti-tumour CD8+T cells, enhancing their anti-tumour function, it increased the proportion of cancer cells with high HSP90B1 expression. Thus, the potential reasons for NACT resistance were identified as: 1) high levels of endoplasmic reticulum stress (ERS) characteristics, 2) high expression of the MDK-NCL ligand-receptor pair between them and exhausted CD8+T cells before NACT, and 3) high expression of the NECTIN2-TIGIT immune ligand-receptor pair between them and exhausted CD8+T cells after NACT. Thus, our study reveals the mechanisms underlying NACT resistance in patients with HGSOC from the perspective of the independent and interactive roles of cancer cells and CD8+T cells. We propose therapeutic strategies targeting the ERS marker HSP90B1 and the immune escape marker MDK before or during NACT, while targeting NECTIN2 blockade after NACT. This approach may offer new insights into combination treatments for patients with HGSOC displaying NACT resistance.

Tumor heterogeneity is associated with poor prognosis and drug resistance, leading to therapeutic failure. Here, we used tumor evolution analysis to determine the intra- and intertumoral heterogeneity of high-grade serous ovarian cancer (HGSOC) and analyze the correlation between tumor heterogeneity and prognosis, as well as chemotherapy response, through single-cell and spatial transcriptomic analysis. We collected and curated 28 HGSOC patients' single-cell transcriptomic data from five datasets. Then, we developed a novel text-mining-based machine-learning approach to deconstruct the evolutionary patterns of tumor cell functions. We then identified key tumor-related genes within different evolutionary branches, characterized the microenvironmental cell compositions that various functional tumor cells depend on, and analyzed the intra- and intertumoral heterogeneity as well as the tumor microenvironments. These analyses were conducted in relation to the prognosis and chemotherapy response in HGSOC patients. We validated our findings in two spatial and seven bulk transcriptomic datasets (total: 1,030 patients). Using transcriptomic clusters as proxies for functional clonality, we identified a significant increase in tumor cell state heterogeneity that was strongly correlated with patient prognosis and treatment response. Furthermore, increased intra- and intertumoral functional clonality was associated with the characteristics of cancer-associated fibroblasts (CAFs). The spatial proximity between CXCL12-positive CAFs and tumor cells, mediated through the CXCL12/CXCR4 interaction, was highly positively correlated with poor prognosis and chemotherapy resistance in HGSOC. Finally, we constructed a panel of 24 genes through statistical modeling that correlate with CXCL12-positive fibroblasts and can predict both prognosis and the response to chemotherapy in HGSOC patients. Our study offers insights into the collective behavior of tumor cell communities in HGSOC, as well as potential drivers of tumor evolution in response to therapy. There was a strong association between CXCL12-positive fibroblasts and tumor progression, as well as treatment outcomes.

High-grade serous ovarian cancer (HGSOC) represents the most lethal subtype of ovarian cancer, largely due to being commonly diagnosed at advanced stages. The early molecular mechanisms underlying ovarian carcinogenesis remain poorly defined, posing challenges to the development of prevention and early detection strategies. Here we dissect the molecular mechanisms of sex steroid hormone signaling throughout the decades-long evolution of HGSOC precursor lesions, which predominantly originate from secretory epithelial cells of fallopian tubes (FT). We also discuss the prognostic significance of sex steroid receptor isoforms and steroid metabolizing enzymes in HGSOCs. Finally, we provide a comprehensive gene expression atlases of sex steroid receptors, steroidogenic, and steroid-metabolizing enzymes across different cell populations in pre- and postmenopausal FTs, and HGSOCs, using published single-cell RNA sequencing datasets. These atlases reveal that secretory epithelial cells and stromal populations in FTs express sex steroid receptors and enzymes responsible for the formation and inactivation of genotoxic estrogen metabolites. In HGSOC, epithelial cells express various HSD17B isoforms and steroid conjugating enzymes, suggesting an enhanced ability to finely regulate the levels of bioactive sex steroids.

Mounting effective immunity against pathogens and tumours relies on the successful metabolic programming of T cells by extracellular fatty acids1-3. Fatty-acid-binding protein 5 (FABP5) has a key role in this process by coordinating the efficient import and trafficking of lipids that fuel mitochondrial respiration to sustain the bioenergetic requirements of protective CD8+ T cells4,5. However, the mechanisms that govern this immunometabolic axis remain unexplored. Here we report that the cytoskeletal organizer transgelin 2 (TAGLN2) is necessary for optimal fatty acid uptake, mitochondrial respiration and anticancer function in CD8+ T cells. TAGLN2 interacts with FABP5 to facilitate its cell surface localization and function in activated CD8+ T cells. Analyses of ovarian cancer specimens revealed that endoplasmic reticulum (ER) stress responses induced by the tumour microenvironment repress TAGLN2 in infiltrating CD8+ T cells, thereby enforcing their dysfunctional state. Restoring TAGLN2 expression in ER-stressed CD8+ T cells increased their lipid uptake, mitochondrial respiration and cytotoxic capacity. Accordingly, chimeric antigen receptor T cells overexpressing TAGLN2 bypassed the detrimental effects of tumour-induced ER stress and demonstrated therapeutic efficacy in mice with metastatic ovarian cancer. Our study establishes the role of cytoskeletal TAGLN2 in T cell lipid metabolism and highlights the potential to enhance cellular immunotherapy in solid malignancies by preserving the TAGLN2-FABP5 axis.

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

Advancements in spatial transcriptomics have transformed our understanding of organ function and tissue microenvironment. However, accurately identifying spatial domains to depict genome heterogeneity and cellular interactions remains a challenge. In this study, we propose EnSDD (Ensemble-learning for Spatial Domain Detection), a method that ingeniously integrates eight state-of-the-art spatial domain detection methods to automatically identify spatial domains. A key innovation of EnSDD is its dynamic weighting mechanism within the ensemble learning process, which optimizes the contribution of each base model and provides a performance evaluation metric without the need for ground truth data. By leveraging the spatial domains identified through EnSDD, we incorporate the detection of domain-specific spatially variable genes and the spatial distribution of cell types, thereby providing deeper insights into tissue heterogeneity. We validate EnSDD across diverse spatial transcriptomics datasets from various tissue organizational structures. Our results demonstrate that EnSDD significantly enhances spatial domain identification accuracy, identifies genes with spatial expression patterns, and reveals domain-specific cell type enrichment patterns, offering invaluable insights into tissue spatial heterogeneity and regionalization.

Recent studies indicate that replication checkpoint modulators (RCMs) such as inhibitors of CHK1, ATR, and WEE1 have promising monotherapy activity in solid tumors, including platinum-resistant high grade serous ovarian cancer (HGSOC). However, clinical response rates are generally below 30%. While RCM-induced DNA damage has been extensively examined in preclinical and clinical studies, the link between replication checkpoint interruption and tumor shrinkage remains incompletely understood. Here we utilized HGSOC cell lines and patient-derived xenografts (PDXs) to study events leading from RCM treatment to ovarian cancer cell death. These studies show that RCMs increase CDC25A levels and CDK2 signaling in vitro, leading to dysregulated cell cycle progression and increased replication stress in HGSOC cell lines independent of homologous recombination status. These events lead to sequential activation of JNK and multiple BH3-only proteins, including BCL2L11/BIM, BBC3/PUMA and the BMF, all of which are required to fully initiate RCM-induced apoptosis. Activation of the same signaling pathway occurs in HGSOC PDXs that are resistant to poly(ADP-ribose) polymerase inhibitors but respond to RCMs ex vivo with a decrease in cell number in 3-dimensional culture and in vivo with xenograft shrinkage or a significantly diminished growth rate. These findings identify key cell death-initiating events that link replication checkpoint inhibition to antitumor response in ovarian cancer.

Platinum resistance in ovarian cancer poses a significant challenge, substantially impacting patient outcomes. Developing an accurate predictive model is crucial for improving clinical decision-making and guiding treatment strategies. Proteomic data from 217 high-grade serous ovarian cancer (HGSOC) biospecimens obtained from JHU, PNNL, and PTRC were used to construct a prediction model for identifying individuals who are resistant to platinum-based chemotherapy. A total of 6437 common proteins were detected across all data sets, with 26 proteins overlapping between the development cohorts JHU and PNNL. Using LASSO and logistic regression analysis, a six-protein model (P31323_PRKAR2B, Q13309_SKP2, Q14997_PSME4, Q6ZRP7_QSOX2, Q7LGA3_HS2ST1, and Q7Z2Z2_EFL1) was developed, which accurately predicted platinum resistance, with an AUC of 0.964 (95% CI, 0.929-0.999). Internal validation by resampling resulted in a C-index of 0.972 (95% CI 0.894-0.988). External validation performed on the PTRC cohort achieved an AUC of 0.855 (95% CI 0.748-0.963). Calibration curves showed good consistency, and DCA indicated superior clinical utility. The model also performed well in predicting PFS and OS at various time points. Based on these proteins, our predictive model can precisely predict platinum response and survival outcomes in HGSOC patients, which can assist clinicians in promptly identifying potentially platinum-resistant individuals.

Iron accumulation in tumors contributes to disease progression and chemoresistance. Although targeting this process can influence various hallmarks of cancer, the immunomodulatory effects of iron chelation in the tumor microenvironment are unknown. Here, we report that treatment with deferiprone, an FDA-approved iron chelator, unleashes innate immune responses that restrain ovarian cancer. Deferiprone reprogrammed ovarian cancer cells toward an immunostimulatory state characterized by the production of type-I IFN and overexpression of molecules that activate NK cells. Mechanistically, these effects were driven by innate sensing of mitochondrial DNA in the cytosol and concomitant activation of nuclear DNA damage responses triggered upon iron chelation. Deferiprone synergized with chemotherapy and prolonged the survival of mice with ovarian cancer by bolstering type-I IFN responses that drove NK cell-dependent control of metastatic disease. Hence, iron chelation may represent an alternative immunotherapeutic strategy for malignancies that are refractory to current T-cell-centric modalities. Significance: This study uncovers that targeting dysregulated iron accumulation in ovarian tumors represents a major therapeutic opportunity. Iron chelation therapy using an FDA-approved agent causes immunogenic stress responses in ovarian cancer cells that delay metastatic disease progression and enhance the effects of first-line chemotherapy. See related commentary by Bell and Zou, p. 1771.

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.

Disease recurrence following chemotherapy is a major clinical challenge in ovarian cancer (OC), but little is known regarding how the tumour epigenome regulates transcriptional programs underpinning chemoresistance. We determine the single cell chromatin accessibility landscape of omental OC metastasis from treatment-naïve and neoadjuvant chemotherapy-treated patients and define the chromatin accessibility profiles of epithelial, fibroblast, myeloid and lymphoid cells. Epithelial tumour cells display open chromatin regions enriched with motifs for the oncogenic transcription factors MEIS and PBX. Post chemotherapy microenvironments show profound tumour heterogeneity and selection for cells with accessible chromatin enriched for TP53, TP63, TWIST1 and resistance-pathway-activating transcription factor binding motifs. An OC chemoresistant tumour subpopulation known to be present prior to treatment, and characterised by stress-associated gene expression, is enriched post chemotherapy. Nuclear receptors RORa, NR2F6 and HNF4G are uncovered as candidate transcriptional drivers of these cells whilst closure of binding sites for E2F2 and E2F4 indicate post-treated tumour having low proliferative capacity. Delineation of the gene regulatory landscape of ovarian cancer cells surviving chemotherapy treatment therefore reveals potential core transcriptional regulators of chemoresistance, suggesting novel therapeutic targets for improving clinical outcome.

The heterogeneity of high-grade serous ovarian carcinoma (HGSOC) has hindered the clinical treatment, and our current study aims to characterize the change in tumor microenvironment (TME) with the progression of HGSOC via single cell RNA sequencing (scRNA-seq).

The single-cell landscape in HGSOC was downloaded from the dataset GSE184880, which included 7 HGSOC and 5 normal samples and then applied for the filtering and annotation of cell clusters. The differentially expressed marker genes in these clusters were analyzed via "FindAllMarker" function in Seurat package and the functional enrichment analyses were implemented using clusterProflier package. Finally, the CellChat package was applied for the cell-cell communication analysis. Cellular experimental were determined Real-time Reverse Transcription Polymerase Chain Reaction (RT-qPCR).

45,448 single cells were categorized into 10 cell clusters. The proportion of NK/T cells (49.5%), epithelial cells (15.3%) and myeloid cells (14%) was higher in the HGSOC samples. The heterogeneity and different enriched pathways of epithelial cells have been revealed with the progression of HGSOC from early to late stage, concurrent with the reduced activity of cytotoxic NK/T cells and the decreased capabilities of recruiting immune cells and presenting antigens in macrophages. Besides, the cell-cell communication analysis has revealed a strong communication of CXCL and CCL signal between M1 macrophages and cytotoxic NK/T cells in early stage of HGSOC. Moreover, RT-qPCR indicated that CCL4/5 and CCR1/5 levels were upregulated in tumor cell SK-OV-3.

The investigation using scRNA-seq has depicted the change in cytotoxic NK/T cells, epithelial cells and myeloid cells of the TME of HGSOC, which may provide another insight into the specific mechanisms underlying the progression of HGSOC.

Over decades, peritoneal surface malignancies (PSMs) have been associated with limited treatment options and poor prognosis. However, advancements in perioperative systemic chemotherapy, cytoreductive surgery (CRS), and hyperthermic intraperitoneal chemotherapy (HIPEC) have significantly improved clinical outcomes. PSMs predominantly result from the spread of intra-abdominal neoplasia, which then form secondary peritoneal metastases. Colorectal, ovarian, and gastric cancers are the most common contributors. Despite diverse primary origins, the uniqueness of the peritoneum microenvironment shapes the common features of PSMs. Peritoneal metastization involves complex interactions between tumour cells and the peritoneal microenvironment. Fibroblasts play a crucial role, contributing to tumour development, progression, and therapy resistance. Peritoneal metastasis-associated fibroblasts (MAFs) in PSMs exhibit high heterogeneity. Single-cell RNA sequencing technology has revealed that immune-regulatory cancer-associated fibroblasts (iCAFs) seem to be the most prevalent subtype in PSMs. In addition, other major subtypes as myofibroblastic CAFs (myCAFs) and matrix CAFs (mCAFs) were frequently observed across PSMs studies. Peritoneal MAFs are suggested to originate from mesothelial cells, submesothelial fibroblasts, pericytes, endothelial cells, and omental-resident cells. This plasticity and heterogeneity of CAFs contribute to the complex microenvironment in PSMs, impacting treatment responses. Understanding these interactions is crucial for developing targeted and local therapies to improve PSMs patient outcomes.

The paradigm for macrophage characterization has evolved from the simple M1/M2 dichotomy to a more complex model that encompasses the broad spectrum of macrophage phenotypic diversity, due to differences in ontogeny and/or local stimuli. We currently lack an in-depth pan-cancer single cell RNA-seq (scRNAseq) atlas of tumour-associated macrophages (TAMs) that fully captures this complexity. In addition, an increased understanding of macrophage diversity could help to explain the variable responses of cancer patients to immunotherapy. Our atlas includes well established macrophage subsets as well as a number of additional ones. We associate macrophage composition with tumour phenotype and show macrophage subsets can vary between primary and metastatic tumours growing in sites like the liver. We also examine macrophage-T cell functional cross talk and identify two subsets of TAMs associated with T cell activation. Analysis of TAM signatures in a large cohort of immune checkpoint inhibitor-treated patients (CPI1000 + ) identify multiple TAM subsets associated with response, including the presence of a subset of TAMs that upregulate collagen-related genes. Finally, we demonstrate the utility of our data as a resource and reference atlas for mapping of novel macrophage datasets using projection. Overall, these advances represent an important step in both macrophage classification and overcoming resistance to immunotherapies in cancer.

Advancements in precision oncology over the past decades have led to new therapeutic interventions, but the efficacy of such treatments is generally limited by an adaptive process that fosters drug resistance1. In addition to genetic mutations2, recent research has identified a role for non-genetic plasticity in transient drug tolerance3 and the acquisition of stable resistance4,5. However, the dynamics of cell-state transitions that occur in the adaptation to cancer therapies remain unknown and require a systems-level longitudinal framework. Here we demonstrate that resistance develops through trajectories of cell-state transitions accompanied by a progressive increase in cell fitness, which we denote as the 'resistance continuum'. This cellular adaptation involves a stepwise assembly of gene expression programmes and epigenetically reinforced cell states underpinned by phenotypic plasticity, adaptation to stress and metabolic reprogramming. Our results support the notion that epithelial-to-mesenchymal transition or stemness programmes-often considered a proxy for phenotypic plasticity-enable adaptation, rather than a full resistance mechanism. Through systematic genetic perturbations, we identify the acquisition of metabolic dependencies, exposing vulnerabilities that can potentially be exploited therapeutically. The concept of the resistance continuum highlights the dynamic nature of cellular adaptation and calls for complementary therapies directed at the mechanisms underlying adaptive cell-state transitions.

Elevated allostatic load (AL), an integrated, cumulative marker of physiologic damage due to socioenvironmental stress, is associated with increased mortality in patients with breast, lung, and other cancers. The relationship between allostatic load and mortality in ovarian cancer patients remains unknown. We examined the relationship between allostatic load and overall survival in ovarian cancer patients.

This cross-sectional study used data from 201 patients enrolled in a prospective observational ovarian cancer cohort study at a National Cancer Institute-designated Comprehensive Cancer Center from October 2012 through June 2022. All patients underwent debulking surgery and completed a full course of standard-of-care platinum-based chemotherapy. Follow-up was completed through January 2024. Allostatic load was calculated as a summary score by assigning one point to the worst sample quartile for each of ten biomarkers measured within 45 days before the ovarian cancer diagnosis. High allostatic load was defined as having an allostatic load in the top quartile of the summary score. A Cox proportional hazard model with robust variance tested the association between allostatic load and overall survival.

There were no associations between allostatic load and ovarian cancer clinical characteristics. After accounting for demographic, clinical, and treatment factors, high allostatic load was associated with a significant increase in mortality (hazard ratio 2.17 [95%CI, 1.13-4.15]; P = 0.02).

Higher allostatic load is associated with worse survival among ovarian cancer patients. Allostatic load could help identify patients at risk for poorer outcomes who may benefit from greater socioenvironmental support during treatment.

Ovarian cancer often develops resistance to conventional therapies, hampering their effectiveness. Here, using ex vivo paired ovarian cancer ascites obtained before and after chemotherapy and in vitro therapy-induced secretomes, we show that molecules secreted by ovarian cancer cells upon therapy promote cisplatin resistance and enhance DNA damage repair in recipient cancer cells. Even a short-term incubation of chemonaive ovarian cancer cells with therapy-induced secretomes induces changes resembling those that are observed in chemoresistant patient-derived tumor cells after long-term therapy. Using integrative omics techniques, we find that both ex vivo and in vitro therapy-induced secretomes are enriched with spliceosomal components, which relocalize from the nucleus to the cytoplasm and subsequently into the extracellular vesicles upon treatment. We demonstrate that these molecules substantially contribute to the phenotypic effects of therapy-induced secretomes. Thus, SNU13 and SYNCRIP spliceosomal proteins promote therapy resistance, while the exogenous U12 and U6atac snRNAs stimulate tumor growth. These findings demonstrate the significance of spliceosomal network perturbation during therapy and further highlight that extracellular signaling might be a key factor contributing to the emergence of ovarian cancer therapy resistance.

Cancer-associated fibroblasts (CAFs) are the primary stromal cells found in tumor microenvironment, and display high plasticity and heterogeneity. By using single-cell RNA-seq technology, researchers have identified various subpopulations of CAFs, particularly highlighting a recently identified subpopulation termed antigen-presenting CAFs (apCAFs), which are largely unknown.

We collected datasets from public databases for 9 different solid tumor types to analyze the role of apCAFs in the tumor microenvironment.

Our data revealed that apCAFs, likely originating mainly from normal fibroblast, are commonly found in different solid tumor types and generally are associated with anti-tumor effects. apCAFs may be associated with the activation of CD4+ effector T cells and potentially promote the survival of CD4+ effector T cells through the expression of C1Q molecules. Moreover, apCAFs exhibited highly enrichment of transcription factors RUNX3 and IKZF1, along with increased glycolytic metabolism.

Taken together, these findings offer novel insights into a deeper understanding of apCAFs and the potential therapeutic implications for apCAFs targeted immunotherapy in cancer.

Single-cell sequencing was employed to analyze the tumor immune microenvironment in ovarian cancer (OC) patients, exploring the evolutionary roles of various macrophage subgroups in OC progression and their correlation with fatty acid metabolism-related genes in contributing to drug resistance.

This study aimed to decipher the mechanisms underlying OC chemoresistance (OC-CR) and carboplatin resistance by integrating and analyzing multiple single-cell RNA sequencing datasets from OC patients. The tumor immune microenvironment in OC-CR patients exhibited notable alterations in cellular interactions and the proportions of different immune cell populations, in contrast to the cohort sensitive to OC chemotherapy.

The study demonstrates that the fatty acid-associated gene HEBP2 not only accelerates OC progression but also modifies the immune landscape of OC, driving the polarization from M0_TAM to M2_TAM. This shift results in a diminished efficacy of chemotherapy in OC. Furthermore, both in vitro and in vivo experiments underscored HEBP2's role in boosting the proliferation of OC-resistant cell lines and suppressing apoptosis, thereby facilitating carboplatin resistance.

In conclusion, the immune microenvironments of OC-CR significantly differ from those sensitive to chemotherapy, underscoring HEBP2's role in fostering OC resistance. This establishes HEBP2 as a promising prognostic marker and a novel target for therapeutic strategies against OC resistance.

Single-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints' scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation studies and applications to a real multi-timepoint single cell dataset, we demonstrate the robust and favorable performance of the proposal versus existing methods serving similar purposes.

Purpose: Platinum-based chemotherapy is effective but limited by resistance in high-grade serous ovarian cancer (HGSOC). Single-cell RNA sequencing (scRNA-seq) can reveal tumour cell heterogeneity and subclonal differentiation. We aimed to analyze resistance mechanisms and potential targets in HGSOC using scRNA-seq. Methods: We performed 10× genomics scRNA-seq sequencing on tumour tissues from 3 platinum-sensitive and 3 platinum-resistant HGSOC patients. We analyzed cell subcluster communication networks and spatial distribution using cellchat. We performed RNA-seq analysis on TACSTD2, a representative resistance gene in the E0 subcluster, to explore its molecular mechanism. Results: Epithelial cells, characterized by distinct chemotherapy resistance traits and highest gene copy number variations, revealed a specific cisplatin-resistant cluster (E0) associated with poor prognosis. E0 exhibited malignant features related to resistance, fostering growth through communication with fibroblasts and endothelial cells. Spatially, E0 promoted fibroblasts to protect tumour cells and impede immune cells infiltration. Furthermore, TACSTD2 was identified as a representative gene of the E0 subcluster, elucidating its role in platinum resistance through the Rap1/PI3K/AKT pathway. Conclusions: Our study reveals a platinum-resistant epithelial cell subcluster E0 and its association with TACSTD2 in HGSOC, uncovers new insights and evidence for the platinum resistance mechanism, and provides new ideas and targets for the development of therapeutic strategies against TACSTD2+ epithelial cancer cells.

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.