Single-cell transcriptomes reveal heterogeneity of high-grade serous ovarian carcinoma

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes.

High expression of SLC7A1 in high-grade serous ovarian cancer promotes tumor progression and is involved in MAPK/ERK pathway and EMT

Our previous studies have shown that upregulation of SLC7A1 in epithelial ovarian cancer (EOC) tumor cells significantly increases cancer cell proliferation, migration, and cisplatin resistance; however, the molecular mechanism by which SLC7A1 functions in EOC remains unknown. In later studies, we found that SLC7A1 is also highly expressed in the interstitial portion of high-grade serous ovarian cancer (HGSOC), but the significance of this high expression in the interstitial remains unclear. Here, we showed the Interstitial high expression of SLC7A1 in HGSOC by immunohistochemistry. SLC7A1 enriched in cancer-associated fibroblasts (CAFs) was upregulated by TGF-β1. Transwell assay, scratch assay, cck8 assay and cell adhesion assay showed that SLC7A1 highly expressed in CAFs promoted tumor cells invasion, migration and metastasis in vitro. The effect of SLC7A1 on MAPK and EMT pathway proteins in ovarian cancer (OC) was verified by RNA sequencing and western blotting. Overexpression of SLC7A1 in OC is involved in MAPK/ ERK pathway and EMT. In general, in HGSOC, CAFs overexpressing SLC7A1 supported the migration and invasion of tumor cells; SLC7A1 is highly expressed in ovarian cancer and is involved in ERK phosphorylation and EMT signaling in MAPK signaling pathway. This suggests that SLC7A1 may be a potential therapeutic target for OC metastasis.

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

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

Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC

Background

High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice.

Methods

We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments.

Results

We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment.

Conclusions

We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.

Paradigm Shift: A Comprehensive Review of Ovarian Cancer Management in an Era of Advancements

Ovarian cancer (OC) is the female genital malignancy with the highest lethality. Patients present a poor prognosis mainly due to the late clinical presentation allied with the common acquisition of chemoresistance and a high rate of tumour recurrence. Effective screening, accurate diagnosis, and personalised multidisciplinary treatments are crucial for improving patients' survival and quality of life. This comprehensive narrative review aims to describe the current knowledge on the aetiology, prevention, diagnosis, and treatment of OC, highlighting the latest significant advancements and future directions. Traditionally, OC treatment involves the combination of cytoreductive surgery and platinum-based chemotherapy. Although more therapeutical approaches have been developed, the lack of established predictive biomarkers to guide disease management has led to only marginal improvements in progression-free survival (PFS) while patients face an increasing level of toxicity. Fortunately, because of a better overall understanding of ovarian tumourigenesis and advancements in the disease's (epi)genetic and molecular profiling, a paradigm shift has emerged with the identification of new disease biomarkers and the proposal of targeted therapeutic approaches to postpone disease recurrence and decrease side effects, while increasing patients' survival. Despite this progress, several challenges in disease management, including disease heterogeneity and drug resistance, still need to be overcome.

Genomic hallmarks and therapeutic targets of ribosome biogenesis in cancer

Hyperactive ribosome biogenesis (RiboSis) fuels unrestricted cell proliferation, whereas genomic hallmarks and therapeutic targets of RiboSis in cancers remain elusive, and efficient approaches to quantify RiboSis activity are still limited. Here, we have established an in silico approach to conveniently score RiboSis activity based on individual transcriptome data. By employing this novel approach and RNA-seq data of 14 645 samples from TCGA/GTEx dataset and 917 294 single-cell expression profiles across 13 cancer types, we observed the elevated activity of RiboSis in malignant cells of various human cancers, and high risk of severe outcomes in patients with high RiboSis activity. Our mining of pan-cancer multi-omics data characterized numerous molecular alterations of RiboSis, and unveiled the predominant somatic alteration in RiboSis genes was copy number variation. A total of 128 RiboSis genes, including EXOSC4, BOP1, RPLP0P6 and UTP23, were identified as potential therapeutic targets. Interestingly, we observed that the activity of RiboSis was associated with TP53 mutations, and hyperactive RiboSis was associated with poor outcomes in lung cancer patients without TP53 mutations, highlighting the importance of considering TP53 mutations during therapy by impairing RiboSis. Moreover, we predicted 23 compounds, including methotrexate and CX-5461, associated with the expression signature of RiboSis genes. The current study generates a comprehensive blueprint of molecular alterations in RiboSis genes across cancers, which provides a valuable resource for RiboSis-based anti-tumor therapy.

Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?

High-grade serous tubo-ovarian cancer (HGSTOC) is the most lethal tumor of the female genital tract. The foregoing therapy consists of cytoreduction followed by standard platinum/taxane chemotherapy; alternatively, for primary unresectable tumors, neo-adjuvant platinum/taxane chemotherapy followed by delayed interval cytoreduction. In patients with suboptimal surgery or advanced disease, different forms of targeted therapy have been accepted or tested in clinical trials. Studies on HGSTOC discovered its genetic and proteomic heterogeneity, epigenetic regulation, and the role of the tumor microenvironment. These findings turned attention to the fact that there are several distinct primary tumor subtypes of HGSTOC and the unique biology of primary, metastatic, and recurrent tumors may result in a differential drug response. This results in both chemo-refractoriness of some primary tumors and, what is significantly more frequent and destructive, secondary chemo-resistance of metastatic and recurrent HGSTOC tumors. Treatment possibilities for platinum-resistant disease include several chemotherapeutics with moderate activity and different targeted drugs with difficult tolerable effects. Therefore, the question appears as to why different subtypes of ovarian cancer are predominantly treated based on the same therapeutic schemes and not in an individualized way, adjusted to the biology of a specific tumor subtype and temporal moment of the disease. The paper reviews the genomic, mutational, and epigenetic signatures of HGSTOC subtypes and the tumor microenvironment. The clinical trials on personalized therapy and the overall results of a new, comprehensive approach to personalized therapy for ovarian cancer have been presented and discussed.

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

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

The selective activator protein-1 inhibitor T-5224 regulates the IRF4/MYC axis and exerts cooperative antimyeloma activity with bortezomib

The activating protein-1 (AP-1) transcription factors (TFs) have been associated with many different cancer types and are promising therapeutic targets in logical malignancies. However, the mechanisms of their role in multiple myeloma (MM) remain elusive. The present study determined and compared the mRNA and protein expression levels of the AP-1 family member JunB in CD138+ mononuclear cells from MM patients and healthy donors. Herein, we investigated the effect of T-5224, an inhibitor of JUN/AP-1, on MM. We found that the cytotoxicity of T-5224 toward myeloma is due to its ability to induce cell apoptosis, inhibit proliferation, and induce cell cycle arrest by increasing the levels of cleaved caspase3/7 and concomitantly inhibiting the IRF4/MYC axis. We also noticed that siJunB-mediated deletion of JunB/AP-1 enhanced MM cell apoptosis and affected cell proliferation. The software PROMO was used in the present study to predict the AP-1 TF that may bind the promoter region of IRF4. We confirmed the correlation between JunB/AP-1 and IRF4. Given that bortezomib (BTZ) facilitates IRF4 degradation in MM cells, we applied combination treatment of BTZ with T-5224. T-5224 and BTZ exerted synergistic effects, and T-5224 reversed the effect of BTZ on CD138+ primary resistance in MM cells, in part due to suppression of the IRF4/MYC axis. Our results suggest that targeting AP-1 TFs is a promising therapeutic strategy for MM. Additionally, targeting both AP-1 and IRF4 with T-5224 may be a synergistic therapeutic strategy for this clinically challenging subset of MM.

Single-cell RNA-sequencing analysis reveals divergent transcriptome events between platinum-sensitive and platinum-resistant high-grade serous ovarian carcinoma

BACKGROUND

Tumor resistance is one of the main reasons leading to the failure of ovarian cancer treatment. Overcoming platinum resistance remains the greatest challenge in the management of high-grade serous ovarian carcinoma (HGSC).

METHODS

Small conditional RNA-sequencing is a powerful method for exploring the complexity of the cellular components and their interactions in the tumor microenvironment. We profiled the transcriptomes of 35,042 cells from two platinum-sensitive and three platinum resistance HGSC clinical cases downloaded from Gene Expression Omnibus (GSE154600) and annotated tumor cells as platinum-resistant or sensitive based on the clinical trait. The study systematically investigated the inter-tumoral (using differential expression analysis, CellChat, and SCENIC) and intra-tumoral heterogeneity (using enrichment analysis such as gene set enrichment analysis, as well as gene set variation analysis, weighted gene correlation network analysis, and Pseudo-time analysis) of HGSC.

RESULTS

A cellular map of HGSC generated by profiling 30,780 cells was revisualized using Uniform Manifold Approximation and Projection. The inter-tumoral heterogeneity was demonstrated with intercellular ligand-receptor interactions of major cell types and regulons networks. FN1, SPP1, and COLLAGEN play important roles in the cross-talk between tumor cells and the tumor microenvironment. HOXA7, HOXA9_extended, TBL1XR1_extended, KLF5, SOX17, and CTCFL regulons consistent with the distribution of platinum-resistant HGSC cells were the high activity regions. The intra-tumoral heterogeneity of HGSC was presented with corresponding functional pathway characteristics, tumor stemness features, and the cellular lineage transition from platinum-sensitive to resistant condition. Epithelial-mesenchymal transition played an important role in platinum resistance, whereas oxidative phosphorylation was the opposite. There was a small subset of cells in platinum-sensitive samples that had transcriptomic characteristics similar to platinum-resistant cells, suggesting that the progression of platinum resistance in ovarian cancer is inevitable.

CONCLUSIONS

The present study describes a view of HGSC at single-cell resolution that reveals the characteristics of the HGSC heterogeneity and provides a useful framework for future investigation of platinum-resistant.

Single-cell transcriptomics identify TNFRSF1B as a novel T-cell exhaustion marker for ovarian cancer

BACKGROUND

Ovarian cancer (OC) patients routinely show poor immunotherapeutic response due to the complex tumour microenvironment (TME). It is urgent to explore new immunotherapeutic markers.

METHODS

Through the single-cell RNA sequencing (scRNA-seq) analyses on high-grade serous OC (HGSOC), moderate severity borderline tumour and matched normal ovary, we identified a novel exhausted T cells subpopulation that related to poor prognosis in OC. Histological staining, multiple immunofluorescences, and flow cytometry were applied to validate some results from scRNA-seq. Furthermore, a tumour-bearing mice model was constructed to investigate the effects of TNFRSF1B treatment on tumour growth in vivo.

RESULTS

Highly immunosuppressive TME in HGSOC is displayed compared to moderate severity borderline tumour and matched normal ovary. Subsequently, a novel exhausted subpopulation of CD8+ TNFRSF1B+ T cells is identified, which is associated with poor survival. In vitro experiments demonstrate that TNFRSF1B is specifically upregulated on activated CD8+ T cells and suppressed interferon-γ secretion. The expression of TNFRSF1B on CD8+ T cells is closely related to OC clinical malignancy and is a marker of poor prognosis through 140 OC patients' verification. In addition, the blockade of TNFRSF1B inhibits tumour growth via profoundly remodeling the immune microenvironment in the OC mouse model.

CONCLUSIONS

Our transcriptomic results analyzed by scRNA-seq delineate a high-resolution snapshot of the entire tumour ecosystem of OC TME. The major applications of our findings were an exhausted subpopulation of CD8+ TNFRSF1B+ T cells for predicting OC patient prognosis and the potential therapeutic value of TNFRSF1B. These findings demonstrated the clinical value of TNFRSF1B as a potential immunotherapy target and extended our understanding of factors contributing to immunotherapy failure in OC.

Identification of potential key genes of TGF-beta signaling associated with the immune response and prognosis of ovarian cancer based on bioinformatics analysis

Background

TGF-beta signaling is a key regulator of immunity and multiple cellular behaviors in cancer. However, the prognostic and therapeutic role of TGF-beta signaling-related genes in ovarian cancer (OV) remains unexplored.

Methods

Data of OV used in the current study were sourced from TCGA and GEO databases. Consensus clustering was applied to classify OV patients into different clusters using TGF-beta signaling-related genes. Differentially expressed genes (DEGs) between different clusters were screened by the "limma" R package. Prognostic genes were screened from DEGs by univariate Cox regression, followed by the construction of the TGF-beta signaling-related score. The prognostic value of TGF-beta signaling-related score was evaluated in both training and testing OV cohorts. Moreover, the immune status, GSEA and therapeutic response between low- and high-score groups were performed to further reveal the potential mechanisms.

Results

By consensus clustering, OV patients were classified into two clusters with different tumor immune environments. After differential expression and univariate Cox regression analyses, GMPR, PIEZO1, EMP1, CXCL13, GADD45B, SORCS2, FOSL2, PODN, LYNX1 and SLC38A5 were selected as prognostic genes. Using PCA algorithm, the TGF-beta signaling-related score of OV patients was calculated based on prognostic genes. Then OV patients were divided into low- and high-TGF-beta signaling-related score groups. We observed that the two score groups had significantly different survivals, tumor immune environments and expressions of immune checkpoints. In addition, GSEA results showed that immune-related pathways and biological processes, like chemokine signaling pathway, TNF signaling pathway and T cell migration were significantly enriched in the low-score group. Moreover, patients in the low- and high-score groups had remarkably different sensitivity to chemo- and immunotherapy.

Conclusion

For the first time, our study identified ten prognostic genes associated with TGF-beta signaling, constructed a prognostic TGF-beta signaling-related score and investigated the effect of TGF-beta signaling-related score on OV immunity and therapy. These findings may enrich our knowledge of the TGF-beta signaling in OV prognosis and help to improve the prognosis prediction and treatment strategies in OV.

Prognostic Value of Lymph Node Parameters in Elderly Patients With Stage III Serous Ovarian Cancer Based on Competing Risk Model

OBJECTIVES

Competing risk models were used in this study. The purpose of this study was to assess the predictive usefulness of lymph node characteristics in elderly patients with stage III serous ovarian cancer.

METHODS

We conducted a retrospective analysis on 148,598 patients from 2010 to 2016 using the surveillance, epidemiology, and end results database. Lymph node characteristics were collected and examined, including the number of lymph nodes retrieved the number of lymph nodes examined (ELN) and the number of positive lymph nodes (PN). Using competing risk models, we evaluated the connection between these variables and overall survival (OS) and disease-specific survival (DSS).

RESULTS

This study included a total of 3457 ovarian cancer patients. Multivariate analysis using the COX proportional hazards model found that ELN>22 was an independent predictive factor for both OS (hazard ratio [HR] [95% CI]=0.688 [0.553 to 0.856], P <0.05) and DSS (HR [95% CI]=0.65 [0.512 to 0.826], P <0.001), PN>8 was identified as a significant risk factor for both OS (HR [95% CI]=0.908 [0.688 to 1.199], P =0.497) and DSS (HR [95% CI]=0.926 [0.684 to 1.254], P =0.62). Subsequently, using the competing risk model, ELN>22 was found to be an independent protective factor for DSS (HR [95% CI]=0.738 [0.574 to 0.949], P =0.018), while PN>8 was identified as a risk factor for DSS (HR [95% CI]=0.999 [0.731 to 1.366], P =1).

CONCLUSIONS

Our findings demonstrate the robustness of the competing risk model to evaluate the results of the COX proportional hazards model analysis.

PreCanCell: An ensemble learning algorithm for predicting cancer and non-cancer cells from single-cell transcriptomes

We propose PreCanCell, a novel algorithm for predicting malignant and non-malignant cells from single-cell transcriptomes. PreCanCell first identifies the differentially expressed genes (DEGs) between malignant and non-malignant cells commonly in five common cancer types-associated single-cell transcriptome datasets. The five common cancer types include renal cell carcinoma (RCC), head and neck squamous cell carcinoma (HNSCC), melanoma, lung adenocarcinoma (LUAD), and breast cancer (BC). With each of the five datasets as the training set and the DEGs as the features, a single cell is classified as malignant or non-malignant by k-NN (k = 5). Finally, the single cell is determined as malignant or non-malignant by the majority vote of the five k-NN classification results. We tested the predictive performance of PreCanCell in 19 single-cell datasets, and reported classification accuracy, sensitivity, specificity, balanced accuracy (the average of sensitivity and specificity) and the area under the receiver operating characteristic curve (AUROC). In all these datasets, PreCanCell achieved above 0.8 accuracy, sensitivity, specificity, balanced accuracy and AUROC. Finally, we compared the predictive performance of PreCanCell with that of seven other algorithms, including CHETAH, SciBet, SCINA, scmap-cell, scmap-cluster, SingleR, and ikarus. Compared to these algorithms, PreCanCell displays the advantages of higher accuracy and simpler implementation. We have developed an R package for the PreCanCell algorithm, which is available at https://github.com/WangX-Lab/PreCanCell.

"DEPHENCE" system-a novel regimen of therapy that is urgently needed in the high-grade serous ovarian cancer-a focus on anti-cancer stem cell and anti-tumor microenvironment targeted therapies

Ovarian cancer, especially high-grade serous type, is the most lethal gynecological malignancy. The lack of screening programs and the scarcity of symptomatology result in the late diagnosis in about 75% of affected women. Despite very demanding and aggressive surgical treatment, multiple-line chemotherapy regimens and both approved and clinically tested targeted therapies, the overall survival of patients is still unsatisfactory and disappointing. Research studies have recently brought some more understanding of the molecular diversity of the ovarian cancer, its unique intraperitoneal biology, the role of cancer stem cells, and the complexity of tumor microenvironment. There is a growing body of evidence that individualization of the treatment adjusted to the molecular and biochemical signature of the tumor as well as to the medical status of the patient should replace or supplement the foregoing therapy. In this review, we have proposed the principles of the novel regimen of the therapy that we called the "DEPHENCE" system, and we have extensively discussed the results of the studies focused on the ovarian cancer stem cells, other components of cancer metastatic niche, and, finally, clinical trials targeting these two environments. Through this, we have tried to present the evolving landscape of treatment options and put flesh on the experimental approach to attack the high-grade serous ovarian cancer multidirectionally, corresponding to the "DEPHENCE" system postulates.

Impaired histone inheritance promotes tumor progression

Faithful inheritance of parental histones is essential to maintain epigenetic information and cellular identity during cell division. Parental histones are evenly deposited onto the replicating DNA of sister chromatids in a process dependent on the MCM2 subunit of DNA helicase. However, the impact of aberrant parental histone partition on human disease such as cancer is largely unknown. In this study, we construct a model of impaired histone inheritance by introducing MCM2-2A mutation (defective in parental histone binding) in MCF-7 breast cancer cells. The resulting impaired histone inheritance reprograms the histone modification landscapes of progeny cells, especially the repressive histone mark H3K27me3. Lower H3K27me3 levels derepress the expression of genes associated with development, cell proliferation, and epithelial to mesenchymal transition. These epigenetic changes confer fitness advantages to some newly emerged subclones and consequently promote tumor growth and metastasis after orthotopic implantation. In summary, our results indicate that impaired inheritance of parental histones can drive tumor progression.

Unraveling the heterogeneity of cholangiocarcinoma and identifying biomarkers and therapeutic strategies with single-cell sequencing technology

Cholangiocarcinoma (CCA) is a common malignant tumor of the biliary tract that carries a high burden of morbidity and a poor prognosis. Due to the lack of precise diagnostic methods, many patients are often diagnosed at advanced stages of the disease. The current treatment options available are of varying efficacy, underscoring the urgency for the discovery of more effective biomarkers for early diagnosis and improved treatment. Recently, single-cell sequencing (SCS) technology has gained popularity in cancer research. This technology has the ability to analyze tumor tissues at the single-cell level, thus providing insights into the genomics and epigenetics of tumor cells. It also serves as a practical approach to study the mechanisms of cancer progression and to explore therapeutic strategies. In this review, we aim to assess the heterogeneity of CCA using single-cell sequencing technology, with the ultimate goal of identifying possible biomarkers and potential treatment targets.

A platinum(II) complex HY1-Pt overcomes cisplatin-induced resistance and attenuates metastasis of epithelial ovarian cancer by cancer cell stemness inhibition

Tumor recurrence, acquired resistance and metastasis have severely limited the effect of clinical treatments for epithelial ovarian cancer. Recent researches reveal that cancer stem cells play important roles in the process of cisplatin-induced resistance and cancer cell metastasis. A platinum(II) complex (HY1-Pt) owning casein kinase 2 specificity reported in our recent research was herein applied to treat cisplatin-sensitive and cisplatin-resistant epithelial ovarian cancers, respectively, anticipating to achieve high anti-tumor efficacy. HY1-Pt showed highly efficient anti-tumor effect with low toxicity for either cisplatin-sensitive or cisplatin-resistant epithelial ovarian cancer both in vitro and in vivo. Biological studies indicated that HY1-Pt as a casein kinase 2 inhibitor could effectively overcome cisplatin resistance through the signaling pathway of Wnt/β-catenin by inhibiting expression of the signature genes of cancer stemness cells in A2780/CDDP cells. Moreover, HY1-Pt could suppress tumor migration and invasion in vitro and in vivo, further proving that HY1-Pt can be a potent novel platinum(II) agent for cisplatin-resistant epithelial ovarian cancer treatment.

Radiomics and Radiogenomics of Ovarian Cancer

Ovarian cancer, one of the deadliest gynecologic malignancies, is characterized by high intra- and inter-site genomic and phenotypic heterogeneity. The traditional information provided by the conventional interpretation of diagnostic imaging studies cannot adequately represent this heterogeneity. Radiomics analyses can capture the complex patterns related to the microstructure of the tissues and provide quantitative information about them. This review outlines how radiomics and its integration with other quantitative biological information, like genomics and proteomics, can impact the clinical management of ovarian cancer.

Regulon active landscape reveals cell development and functional state changes of human primary osteoblasts in vivo

BACKGROUND

While transcription factor (TF) regulation is known to play an important role in osteoblast development, differentiation, and bone metabolism, the molecular features of TFs in human osteoblasts at the single-cell resolution level have not yet been characterized. Here, we identified modules (regulons) of co-regulated genes by applying single-cell regulatory network inference and clustering to the single-cell RNA sequencing profiles of human osteoblasts. We also performed cell-specific network (CSN) analysis, reconstructed regulon activity-based osteoblast development trajectories, and validated the functions of important regulons both in vivo and in vitro.

RESULTS

We identified four cell clusters: preosteoblast-S1, preosteoblast-S2, intermediate osteoblasts, and mature osteoblasts. CSN analysis results and regulon activity-based osteoblast development trajectories revealed cell development and functional state changes of osteoblasts. CREM and FOSL2 regulons were mainly active in preosteoblast-S1, FOXC2 regulons were mainly active in intermediate osteoblast, and RUNX2 and CREB3L1 regulons were most active in mature osteoblasts.

CONCLUSIONS

This is the first study to describe the unique features of human osteoblasts in vivo based on cellular regulon active landscapes. Functional state changes of CREM, FOSL2, FOXC2, RUNX2, and CREB3L1 regulons regarding immunity, cell proliferation, and differentiation identified the important cell stages or subtypes that may be predominantly affected by bone metabolism disorders. These findings may lead to a deeper understanding of the mechanisms underlying bone metabolism and associated diseases.

Tumor microenvironment in ovarian cancer peritoneal metastasis

Ovarian cancer (OC) is one of the most common gynecological malignancies with high morbidity and mortality. The peritoneum is one of the most common metastatic sites in ovarian cancer, involving large amounts of ascites. However, its mechanism is unclear. The peritoneal microenvironment composed of peritoneal effusion and peritoneum creates favorable conditions for ovarian cancer progression and metastasis. Here, we reviewed the peritoneal metastasis patterns and molecular mechanisms of ovarian cancer, as well as major components of the peritoneal microenvironment, peritoneal effusion, and immune microenvironment, and investigated the relationship between the peritoneal microenvironment and ovarian cancer metastasis.

Inferring cancer common and specific gene networks via multi-layer joint graphical model

Cancer is a complex disease caused primarily by genetic variants. Reconstructing gene networks within tumors is essential for understanding the functional regulatory mechanisms of carcinogenesis. Advances in high-throughput sequencing technologies have provided tremendous opportunities for inferring gene networks via computational approaches. However, due to the heterogeneity of the same cancer type and the similarities between different cancer types, it remains a challenge to systematically investigate the commonalities and specificities between gene networks of different cancer types, which is a crucial step towards precision cancer diagnosis and treatment. In this study, we propose a new sparse regularized multi-layer decomposition graphical model to jointly estimate the gene networks of multiple cancer types. Our model can handle various types of gene expression data and decomposes each cancer-type-specific network into three components, i.e., globally shared, partially shared and cancer-type-unique components. By identifying the globally and partially shared gene network components, our model can explore the heterogeneous similarities between different cancer types, and our identified cancer-type-unique components can help to reveal the regulatory mechanisms unique to each cancer type. Extensive experiments on synthetic data illustrate the effectiveness of our model in joint estimation of multiple gene networks. We also apply our model to two real data sets to infer the gene networks of multiple cancer subtypes or cell lines. By analyzing our estimated globally shared, partially shared, and cancer-type-unique components, we identified a number of important genes associated with common and specific regulatory mechanisms across different cancer types.

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

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

SIGNIFICANCE

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

Molecular, cellular and systemic aspects of epithelial ovarian cancer and its tumor microenvironment

Ovarian cancer encompasses a heterogeneous group of malignancies that involve the ovaries, fallopian tubes and the peritoneal cavity. Despite major advances made within the field of cancer, the majority of patients with ovarian cancer are still being diagnosed at an advanced stage of the disease due to lack of effective screening tools. The overall survival of these patients has, therefore, not substantially improved over the past decades. Most patients undergo debulking surgery and treatment with chemotherapy, but often micrometastases remain and acquire resistance to the therapy, eventually leading to disease recurrence. Here, we summarize the current knowledge in epithelial ovarian cancer development and metastatic progression. For the most common subtypes, we focus further on the properties and functions of the immunosuppressive tumor microenvironment, including the extracellular matrix. Current and future treatment modalities are discussed and finally we provide an overview of the different experimental models used to develop novel therapies.

Single-cell sequencing reveals effects of chemotherapy on the immune landscape and TCR/BCR clonal expansion in a relapsed ovarian cancer patient

Cancer recurrence and chemoresistance are the leading causes of death in high-grade serous ovarian cancer (HGSOC) patients. However, the unique role of the immune environment in tumor progression for relapsed chemo-resistant patients remains elusive. In single-cell resolution, we characterized a comprehensive multi-dimensional cellular and immunological atlas from tumor, ascites, and peripheral blood of a chemo-resistant patient at different stages of treatment. Our results highlight a role in recurrence and chemoresistance of the immunosuppressive microenvironment in ascites, including MDSC-like myeloid and hypo-metabolic γδT cells, and of peripheral CD8⁺ effector T cells with chemotherapy-induced senescent/exhaustive. Importantly, paired TCR/BCR sequencing demonstrated relative conservation of TCR clonal expansion in hyper-expanded CD8⁺ T cells and extensive BCR clonal expansion without usage bias of V(D)J genes after chemotherapy. Thus, our study suggests strategies for ameliorating chemotherapy-induced immune impairment to improve the clinical outcome of HGSOC.

Deciphering the immune landscape dominated by cancer-associated fibroblasts to investigate their potential in indicating prognosis and guiding therapeutic regimens in high grade serous ovarian carcinoma

Limited immunotherapeutic effect in high-grade serous ovarian carcinoma (HGSOC) propels exploration of the mechanics behind this resistance, which may be partly elucidated by investigating characters of cancer-associated fibroblasts (CAFs), a significant population in HGSOC involved in shaping tumor immune microenvironment. Herein, leveraging gene expression data of HGSOC samples from The Cancer Genome Atlas and Gene Expression Omnibus datasets, we suggested that CAFs detrimentally affected the outcomes of HGSOC patients. Subsequently, we performed weighted gene co-expression network analysis (WGCNA) to identify a CAFs-related module and screened out seven hub genes from this module, all of which were positively correlated with the infiltration of immunosuppressive macrophages. As one of the hub genes, the expression of fibrillin 1 (FBN1) and its relevance to CD206 were further verified by immunohistochemistry staining in HGSOC samples. Meanwhile, we extracted genes that correlated well with CAF signatures to construct a CAFscore. The capacity of the CAFscore as an independent prognostic factor was validated by Cox regression analyses, and its relevance to components as well as signals in the tumor immune microenvironment was also investigated. Under the evaluation by the CAFscore, HGSOC patients with relatively high CAFscore had worse outcomes, activated mesenchymal signaling pathways, and immune checkpoint blockade (ICB) resistance signatures, which was consistent with the fact that non-responders in anti-PD-1 treatment cohorts tended to have higher CAFscore. Besides, the possibility of CAFscore to guide the selection of sensitive chemotherapeutic agents was explored. In conclusion, individualized assessment of the CAFscore could uncover the extent of stroma activation and immunosuppression and inform therapeutic strategies to improve the benefit of therapies.

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

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

Identification of a Recurrence Gene Signature for Ovarian Cancer Prognosis by Integrating Single-Cell RNA Sequencing and Bulk Expression Datasets

Ovarian cancer is one of the most common gynecological malignancies in women, with a poor prognosis and high mortality. With the expansion of single-cell RNA sequencing technologies, the inner biological mechanism involved in tumor recurrence should be explored at the single-cell level, and novel prognostic signatures derived from recurrence events were urgently identified. In this study, we identified recurrence-related genes for ovarian cancer by integrating two Gene Expression Omnibus datasets, including an ovarian cancer single-cell RNA sequencing dataset (GSE146026) and a bulk expression dataset (GSE44104). Based on these recurrence genes, we further utilized the merged expression dataset containing a total of 524 ovarian cancer samples to identify prognostic signatures and constructed a 13-gene risk model, named RMGS (recurrence marker gene signature). Based on the RMGS score, the samples were stratified into high-risk and low-risk groups, and these two groups displayed significant survival difference in two independent validation cohorts including The Cancer Genome Atlas (TCGA). Also, the RMGS score remained significantly independent in multivariate analysis after adjusting for clinical factors, including the tumor grade and stage. Furthermore, there existed close associations between the RMGS score and immune characterizations, including checkpoint inhibition, EMT signature, and T-cell infiltration. Finally, the associations between RMGS scores and molecular subtypes revealed that samples with mesenchymal subtypes displayed higher RMGS scores. In the meanwhile, the genomics characterization from these two risk groups was also identified. In conclusion, the recurrence-related RMGS model we identified could provide a new understanding of ovarian cancer prognosis at the single-cell level and offer a reference for therapy decisions for patient treatment.

Dissecting Immunosuppressive Cell Communication Patterns Reveals JunB Proto-Oncogene (JUNB) Shaping a Non-Inflamed Tumor Microenvironment

Background: Immunosuppressive cell interactions are responsible for tumor progression and metastasis, as well as anti-tumor immune dysfunction. However, the communication pattern remains unclear.

Methods: We first integrated two single-cell RNA-seq datasets (GSE72056 and GSE103322) of different tumor types to increase the diversity of immunosuppressive cells. Then, based on the analysis results of the communication network, gene regulatory network (GRN), and highly activated pathways, we identified the hub gene in the immunosuppressive tumor microenvironment (TME). To further explore the molecular features of the identified gene, we performed several in silico analysis and in vitro experiments including qRT-PCR and CCK-8 assay.

Results: Four types of immunosuppressive cells were identified, including cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), tumor-associated neutrophils (TANs), and regulatory T cells (Tregs). Based on GRNs and the interactions of immunosuppressive cells and tumor cells, we constructed an intercellular communication signature that divided the pan-cancer TME into two clusters with distinct immunological features and different responses to immunotherapy. In combination with pathway analysis, JunB proto-oncogene (JUNB) was identified as the hub gene of the immunosuppressive TME, and it designed a non-inflamed TME of bladder cancer according to evidence that JUNB was negatively correlated with immunomodulators, chemokines, major histocompatibility complex molecules, immune cell infiltration abundances, anti-cancer immune response, and immune checkpoint inhibitors. Moreover, JUNB may predict an unfavorable response to immunotherapy. The signaling network of the four types of cells demonstrated the dominant roles of CAFs and TAMs in the TME. Further investigation uncovered that the complement signal was highly activated in the interactions between subpopulations of the inflammatory phenotype of CAFs and TAMs. Functional experiment results demonstrated the upregulated JUNB in bladder cancer tissues and low-immunity-score tissues. In addition, CAFs showed a pro-tumor proliferation effect via JUNB.

Conclusion: Our findings gave insights into the immunosuppressive TME communication network and provided potential therapeutic targets.

Identification of an Immune Gene-Based Cisplatin Response Model and CD27 as a Therapeutic Target against Cisplatin Resistance for Ovarian Cancer

Objective. Evidence demonstrates that the immune microenvironment is extensively associated with chemotherapy response of ovarian cancer (OV). Herein, this study is aimed at establishing a cisplatin response prediction model for OV on the basis of immune genes. Methods. The expression profiles of cisplatin-sensitive and cisplatin-resistant OV specimens were integrated from multiple public datasets. The abundance scores of 22 immune cells were estimated with CIBERSORT algorithm. Differentially expressed immune genes (DEGs) were determined between cisplatin-sensitive and cisplatin-resistant groups. Thereafter, a cisplatin response model was constructed based on prognostic DEGs with logistic regression analysis. The prediction performance was validated in independent cohorts. The possible relationships between the model and immunotherapy were then assessed. Results. Treg scores were significantly decreased in cisplatin-resistant than cisplatin-sensitive OV specimens, with the opposite results for naïve B cells and activated dendritic cells. Fourteen prognostic DEGs were identified and used to develop a cisplatin-response model. The response scores, estimated by the model, showed favorable performance in discriminating cisplatin-response and nonresponse samples. The response scores also presented significantly negative correlations with three well-known cisplatin-resistant pathways and a positive correlation with the expression of CD274 (PD-L1). Moreover, the decreased CD27 expression was observed in cisplatin-resistant groups, and OV specimens with higher CD27 expressions were more sensitive to cisplatin treatment. Conclusion. Altogether, our findings proposed a cisplatin response prediction model and identified CD27 that might be involved in cisplatin resistance. Further investigations suggested that CD27 could be a promising immunotherapeutic target for cisplatin-resistant subset of OV.

Integration of Genomic Profiling and Organoid Development in Precision Oncology

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