Spatial heterogeneity of infiltrating T cells in high-grade serous ovarian cancer revealed by multi-omics analysis

UBE2J1 is identified as a novel plasma cell-related gene involved in the prognosis of high-grade serous ovarian cancer

BACKGROUND

Immune cells within tumor tissues play important roles in remodeling the tumor microenvironment, thus affecting tumor progression and the therapeutic response. The current study was designed to identify key markers of plasma cells and explore their role in high-grade serous ovarian cancer (HGSOC).

METHODS

We utilized single-cell sequencing data from the Gene Expression Omnibus (GEO) database to identify key immune cell types within HGSOC tissues and to extract related markers via the Seurat package. The effects of immune cell markers on prognosis were analyzed via univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and gene set variation analysis (GSVA) of bulk sequencing data from The Cancer Genome Atlas (TCGA)-HGSOC cohort. Finally, the effects of key markers on HGSOC cells were evaluated via Cell Counting Kit-8 (CCK-8), Transwell, colony formation, wound healing, immunofluorescence and in vivo tumor growth assays.

RESULTS

At the single-cell level, we detected a significant increase in the proportion of plasma cells in HGSOC samples compared to that in normal ovarian samples. Within HGSOC tissues, these plasma cells were found to interact with CD8 + T cells, fibroblasts and endothelial cells. In addition, patients in the high-plasma cell-related score group had better survival rates and higher epithelial‒mesenchymal transition (EMT), apoptosis and immune scores. Moreover, univariate Cox and LASSO regression analyses revealed that ubiquitin-conjugating enzyme E2 J1 (UBE2J1) is a prognostic marker in HGSOC. Further functional studies revealed that overexpression of UBE2J1 promoted cell proliferation, invasion, migration and colony formation, whereas UBE2J1 knockdown attenuated the abovementioned cellular behaviors. Additionally, UBE2J1 overexpression promoted EMT, as evidenced by alterations in the protein expression levels of N-cadherin, snail family transcriptional repressor 2 (Slug), Twist family BHLH transcription factor 1 (Twist 1) and E-cadherin. Moreover, we found that UBE2J1 silencing was able to inhibit the tumor growth in vivo.

CONCLUSIONS

Overall, this study elucidated the role of plasma cells and revealed UBE2J1 as a novel oncogene in HGSOC, uncovering new mechanisms related to HGSOC tumorigenesis and promising therapeutic targets for HGSOC patients.

Characterizing tumor biology and immune microenvironment in high-grade serous ovarian cancer via single-cell RNA sequencing: insights for targeted and personalized immunotherapy strategies

Background

High-grade serous ovarian cancer (HGSOC), the predominant subtype of epithelial ovarian cancer, is frequently diagnosed at an advanced stage due to its nonspecific early symptoms. Despite standard treatments, including cytoreductive surgery and platinum-based chemotherapy, significant improvements in survival have been limited. Understanding the molecular mechanisms, immune landscape, and drug sensitivity of HGSOC is crucial for developing more effective and personalized therapies. This study integrates insights from cancer immunology, molecular profiling, and drug sensitivity analysis to identify novel therapeutic targets and improve treatment outcomes. Utilizing single-cell RNA sequencing (scRNA-seq), the study systematically examines tumor heterogeneity and immune microenvironment, focusing on biomarkers influencing drug response and immune activity, aiming to enhance patient outcomes and quality of life.

Methods

scRNA-seq data was obtained from the GEO database in this study. Differential gene expression was analyzed using gene ontology and gene set enrichment methods. InferCNV identified malignant epithelial cells, while Monocle, Cytotrace, and Slingshot software inferred subtype differentiation trajectories. The CellChat software package predicted cellular communication between malignant cell subtypes and other cells, while pySCENIC analysis was utilized to identify transcription factor regulatory networks within malignant cell subtypes. Finally, the analysis results were validated through functional experiments, and a prognostic model was developed to assess prognosis, immune infiltration, and drug sensitivity across various risk groups.

Results

This study investigated the cellular heterogeneity of HGSOC using scRNA-seq, focusing on tumor cell subtypes and their interactions within the tumor microenvironment. We confirmed the key role of the C2 IGF2+ tumor cell subtype in HGSOC, which was significantly associated with poor prognosis and high levels of chromosomal copy number variations. This subtype was located at the terminal differentiation of the tumor, displaying a higher degree of malignancy and close association with stage IIIC tissue types. The C2 subtype was also associated with various metabolic pathways, such as glycolysis and riboflavin metabolism, as well as programmed cell death processes. The study highlighted the complex interactions between the C2 subtype and fibroblasts through the MK signaling pathway, which may be closely related to tumor-associated fibroblasts and tumor progression. Elevated expression of PRRX1 was significantly connected to the C2 subtype and may impact disease progression by modulating gene transcription. A prognostic model based on the C2 subtype demonstrated its association with adverse prognosis outcomes, emphasizing the importance of immune infiltration and drug sensitivity analysis in clinical intervention strategies.

Conclusion

This study integrates molecular oncology, immunotherapy, and drug sensitivity analysis to reveal the mechanisms driving HGSOC progression and treatment resistance. The C2 IGF2+ tumor subtype, linked to poor prognosis, offers a promising target for future therapies. Emphasizing immune infiltration and drug sensitivity, the research highlights personalized strategies to improve survival and quality of life for HGSOC patients.

Cancer immune evasion, immunoediting and intratumour heterogeneity

Cancers can avoid immune-mediated elimination by acquiring traits that disrupt antitumour immunity. These mechanisms of immune evasion are selected and reinforced during tumour evolution under immune pressure. Some immunogenic subclones are effectively eliminated by antitumour T cell responses (a process known as immunoediting), which results in a clonally selected tumour. Other cancer cells arise to resist immunoediting, which leads to a tumour that includes several distinct cancer cell populations (referred to as intratumour heterogeneity (ITH)). Tumours with high ITH are associated with poor patient outcomes and a lack of responsiveness to immune checkpoint blockade therapy. In this Review, we discuss the different ways that cancer cells evade the immune system and how these mechanisms impact immunoediting and tumour evolution. We also describe how subclonal antigen presentation in tumours with high ITH can result in immune evasion.

Multi-omics Analysis Reveals Key Gut Microbiota and Metabolites Closely Associated with Huntington's Disease

Dysbiosis of the gut microbiota is closely associated with neurodegenerative diseases, including Huntington's disease (HD). Gut microbiome-derived metabolites are key factors in host-microbiome interactions. This study aimed to investigate the crucial gut microbiome and metabolites in HD and their correlations. Fecal and serum samples from 11 to 26 patients with HD, respectively, and 16 and 23 healthy controls, respectively, were collected. The fecal samples were used for shotgun metagenomics while the serum samples for metabolomics analysis. Integrated analysis of the metagenomics and metabolomics data was also conducted. Firmicutes, Bacteroidota, Proteobacteria, Uroviricota, Actinobacteria, and Verrucomicrobia were the dominant phyla. At the genus level, the presence of Bacteroides, Faecalibacterium, Parabacteroides, Alistipes, Dialister, and Christensenella was higher in HD patients, while the abundance of Lachnospira, Roseburia, Clostridium, Ruminococcus, Blautia, Butyricicoccus, Agathobaculum, Phocaeicola, Coprococcus, and Fusicatenibacter decreased. A total of 244 differential metabolites were identified and found to be enriched in the glycerophospholipid, nucleotide, biotin, galactose, and alpha-linolenic acid metabolic pathways. The AUC value from the integrated analysis (1) was higher than that from the analysis of the gut microbiota (0.8632). No significant differences were found in the ACE, Simpson, Shannon, Sobs, and Chao indexes between HD patients and controls. Our study determined crucial functional gut microbiota and potential biomarkers associated with HD pathogenesis, providing new insights into the role of the gut microbiota-brain axis in HD occurrence and development.

Exploration of the shared pathways and common biomarkers in cervical and ovarian cancer using integrated bioinformatics analysis

OBJECTIVE

Searching for potential biomarkers and therapeutic targets for early diagnosis of gynecological tumors to improve patient survival.

METHODS

Microarray datasets of cervical cancer (CC) and ovarian cancer (OC) were downloaded from the Gene Expression Omnibus (GEO) database, then, differential gene expression between cancerous and normal tissues in the datasets was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to screen for co-expression modules associated with CC and OC. The screened shared genes were then further analyzed for functional pathway enrichment. Next, the least absolute shrinkage and selection operator (LASSO) with tenfold cross validation is used to further screened for common diagnostic biomarkers for the two diseases, and further validation is performed using two independent GEO datasets. Finally, the CIBERSORT algorithm was used to estimate the immune infiltration levels of CC and OC, and the correlation between immune cell infiltration and common biomarkers was explored.

RESULTS

After crossing the common DEGs detected by "Limma" R package with the common module genes identified by WGCNA, 44 shared genes were obtained. Functional enrichment indicates that these shared genes are mainly related to DNA synthesis pathways. Lasso regression analysis revealed that EFNA1, TYMS, and WISP2 were co-diagnostic markers for CC and OC, and then based on their expression levels and diagnostic efficacy, EFNA1 was selected as the best co-marker for CC and OC. Immune infiltration analysis shows that the immune environment has a significant impact on the occurrence and development of CC and OC, and the expression of EFNA1 is related to changes in immune cells. Gene-drug interaction analyses identified 27 common drug compounds that interact with candidate genes.

CONCLUSION

This study adopted bioinformatics methods to investigate the common pathways and identify diagnostic markers between CC and OC, suggesting that DNA synthesis and immune environment are closely related to the occurrence and development of CC and OC. EFNA1 may be a potential diagnostic indicator and therapeutic target for patients with CC and OC.

Differential Infiltration of Key Immune T-Cell Populations Across Malignancies Varying by Immunogenic Potential and the Likelihood of Response to Immunotherapy

Background: Solid tumors vary by the immunogenic potential of the tumor microenvironment (TME) and the likelihood of response to immunotherapy. The emerging literature has identified key immune cell populations that significantly impact immune activation or suppression within the TME. This study investigated candidate T-cell populations and their differential infiltration within different tumor types as estimated from mRNA co-expression levels of the corresponding cellular markers. Methods: We analyzed the mRNA co-expression levels of cellular biomarkers that define stem-like tumor-infiltrating lymphocytes (TILs), tissue-resident memory T-cells (TRM), early dysfunctional T-cells, late dysfunctional T-cells, activated-potentially anti-tumor (APA) T-cells and Butyrophilin 3A (BTN3A) isoforms, utilizing clinical and transcriptomic data from 1892 patients diagnosed with melanoma, bladder, ovarian, or pancreatic carcinomas. Real-world data were collected under the Total Cancer Care Protocol and the Avatar® project (NCT03977402) across 18 cancer centers. Furthermore, we compared the survival outcomes following immune checkpoint inhibitors (ICIs) based on immune cell gene expression. Results: In melanoma and bladder cancer, the estimated infiltration of APA T-cells differed significantly (p = 4.67 × 10-12 and p = 5.80 × 10-12, respectively) compared to ovarian and pancreatic cancers. Ovarian cancer had lower TRM T-cell infiltration than melanoma, bladder, and pancreatic (p = 2.23 × 10-8, 3.86 × 10-28, and 7.85 × 10-9, respectively). Similar trends were noted with stem-like, early, and late dysfunctional T-cells. Melanoma and ovarian expressed BTN3A isoforms more than other malignancies. Higher densities of stem-like TILs; TRM, early and late dysfunctional T-cells; APA T-cells; and BTN3A isoforms were associated with increased survival in melanoma (p = 0.0075, 0.00059, 0.013, 0.005, 0.0016, and 0.041, respectively). The TRM gene signature was a moderate predictor of survival in the melanoma cohort (AUROC = 0.65), with similar findings in testing independent public datasets of ICI-treated patients with melanoma (AUROC 0.61-0.64). Conclusions: Key cellular elements related to immune activation are more heavily infiltrated within ICI-responsive versus non-responsive malignancies, supporting a central role in anti-tumor immunity. In melanoma patients treated with ICIs, higher densities of stem-like TILs, TRM T-cells, early dysfunctional T-cells, late dysfunctional T-cells, APA T-cells, and BTN3A isoforms were associated with improved survival.

Memory inflation: Beyond the acute phase of viral infection

Memory inflation is confirmed as the most commonly dysregulation of host immunity with antigen-independent manner in mammals after viral infection. By generating large numbers of effector/memory and terminal differentiated effector memory CD8+ T cells with diminished naïve subsets, memory inflation is believed to play critical roles in connecting the viral infection and the onset of multiple diseases. Here, we reviewed the current understanding of memory inflated CD8+ T cells in their distinct phenotypic features that different from exhausted subsets; the intrinsic and extrinsic roles in regulating the formation of memory inflation; and the key proteins in maintaining the expansion and proliferation of inflationary populations. More importantly, based on the evidences from both clinic and animal models, we summarized the potential mechanisms of memory inflation to trigger autoimmune neuropathies, such as Guillain-Barré syndrome and multiple sclerosis; the correlations of memory inflation between tumorigenesis and resistance of tumour immunotherapies; as well as the effects of memory inflation to facilitate vascular disease progression. To sum up, better understanding of memory inflation could provide us an opportunity to beyond the acute phase of viral infection, and shed a light on the long-term influences of CD8+ T cell heterogeneity in dampen host immune homeostasis.

Single-cell sequencing reveals PTX3 involvement in ovarian cancer metastasis

BACKGROUND

Pentraxin 3 (PTX3) has been associated with the development and progression of various malignant tumors. However, its roles and the mechanisms underlying its involvement in ovarian cancer (OC) peritoneal metastasis remain unclear.

METHODS

Single-cell RNA sequencing (scRNA-seq) and immunohistochemistry (IHC) were conducted to determine the expression profiles, potential functionalities, and underlying mechanisms of PTX3 within the context of OC. To assess the proliferative response of OC cells, we utilized both EdU (5-ethynyl-2' -deoxyuridine) and CCK8 assays. The role of PTX3 in facilitating cell migration and invasion was quantified through the use of Transwell assays. The protein expression levels were meticulously analyzed via Western blotting. Furthermore, to explore the interactions between proteins, we conducted immunofluorescence (IF) staining and co-immunoprecipitation (Co-IP) experiments. To determine the factors responsible for the upregulation of PTX3, we performed both coculture and suspension assays, providing a comprehensive approach to understanding the regulatory mechanisms involved.

RESULTS

This study confirmed, for the first time, that the expression of PTX3 in OC metastatic lesions is greater than that in primary lesions and that tumor cells with high PTX3 expression have greater metastatic ability. PTX3 can activate the EMT and NF-κB signaling pathways in OC cells and can interact with the TLR4 and CD44 receptors in OC cells. Additionally, PTX3's modulation of the EMT and NF-κB pathways is partially dependent on its interaction with TLR4. Furthermore, this study revealed the intercellular regulatory network related to PTX3 in OC cells via bioinformatic analysis. High levels of PTX3 in OC cells potentially enhance the attraction of dendritic cells (DCs) and CD4 + T cells while diminishing the recruitment of B cells and CD8 + T cells. Finally, this study indicated that PTX3 upregulation was driven by multiple factors, including specific transcription factors (TFs) and modifications within the tumor microenvironment (TME).

CONCLUSIONS

Our research revealed the contribution of PTX3 to the peritoneal dissemination process in OC patients, identifying a novel potential biomarker and therapeutic target for this disease.

The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma

Ovarian cancer is the deadliest gynecologic malignancy, and therapeutic options and mortality rates over the last three decades have largely not changed. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes. To improve spatial understanding of the TIME, we performed multiplexed ion beam imaging on 83 human high-grade serous carcinoma tumor samples, identifying approximately 160,000 cells across 23 cell types. From the 77 of these samples that met inclusion criteria, we generated composition features based on cell type proportions, spatial features based on the distances between cell types, and spatial network features representing cell interactions and cell clustering patterns, which we linked to traditional clinical and IHC variables and patient overall survival (OS) and progression-free survival (PFS) outcomes. Among these features, we found several significant univariate correlations, including B-cell contact with M1 macrophages (OS HR = 0.696; P = 0.011; PFS HR = 0.734; P = 0.039). We then used high-dimensional random forest models to evaluate out-of-sample predictive performance for OS and PFS outcomes and to derive relative feature importance scores for each feature. The top model for predicting low or high PFS used TIME composition and spatial features and achieved an average AUC score of 0.71. The results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

SLAMF7 predicts prognosis and correlates with immune infiltration in serous ovarian carcinoma

OBJECTIVE

Signaling lymphocytic activation molecule family members (SLAMFs) play a critical role in immune regulation of malignancies. This study aims to investigate the prognostic value and function of SLAMFs in ovarian cancer (OC).

METHODS

The expression analysis of SLAMFs was conducted based on The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV) and Gene Expression Omnibus (GEO) databases. Immunohistochemistry (IHC) was further performed on tissue arrays (n=98) to determine the expression of SLAMF7. Kaplan-Meier plotter and multivariate Cox regression model were used to evaluate the correlation of SLAMF7 expression with survival outcomes of patients. The molecular function of SLAMF7 in OC was further investigated using Gene Set Enrichment Analysis (GSEA).

RESULTS

SLAMF7 mRNA expression were significantly upregulated in OC tumor tissue compared to normal tissue. IHC revealed that SLAMF7 expression was located in the interstitial parts of tumor tissue, and higher SLAMF7 expression was associated with favorable survival outcomes. GSEA demonstrated that SLAMF7 is involved immune-related pathways. Further analysis showed that SLAMF7 had a strong correlation with the T cell-specific biomarker (CD3) but not with the B cell (CD19, CD22, and CD23) and natural killer cell-specific biomarkers (CD85C, CD336, and CD337). Furthermore, IHC analysis confirmed that SLAMF7 was expressed in tumor-infiltrating T cells, and the IHC score of SLAMF7 was positively correlated with CD3 (r=0.85, p<0.001).

CONCLUSION

SLAMF7 is expressed in the interstitial components of clinical OC tissue, and higher SLAMF7 expression indicated a favorable prognosis for patients with OC. Additionally, SLAMF7 is involved in T-cell immune infiltration in OC.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

Single-cell RNA sequencing reveals the change in cytotoxic NK/T cells, epithelial cells and myeloid cells of the tumor microenvironment of high-grade serous ovarian carcinoma

BACKGROUND

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).

METHODS

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).

RESULTS

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.

CONCLUSION

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.

Immunotherapy and the ovarian cancer microenvironment: Exploring potential strategies for enhanced treatment efficacy

Despite progress in cancer immunotherapy, ovarian cancer (OC) prognosis continues to be disappointing. Recent studies have shed light on how not just tumour cells, but also the complex tumour microenvironment, contribute to this unfavourable outcome of OC immunotherapy. The complexities of the immune microenvironment categorize OC as a 'cold tumour'. Nonetheless, understanding the precise mechanisms through which the microenvironment influences the effectiveness of OC immunotherapy remains an ongoing scientific endeavour. This review primarily aims to dissect the inherent characteristics and behaviours of diverse cells within the immune microenvironment, along with an exploration into its reprogramming and metabolic changes. It is expected that these insights will elucidate the operational dynamics of the immune microenvironment in OC and lay a theoretical groundwork for improving the efficacy of immunotherapy in OC management.

Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq

Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.

Integrated Analysis Reveals COL4A3 as a Novel Diagnostic and Therapeutic Target in UV-Related Skin Cutaneous Melanoma

Background

High levels of UV exposure are a significant factor that can trigger the onset and progression of SKCM. Moreover, this exposure is closely linked to the malignancy of the tumor and the prognosis of patients. Our objective is to identify a tumor biomarker database associated with UV exposure, which can be utilized for prognostic analysis and diagnosis and treatment of SKCM.

Methods

This study used the weighted gene co-expression network analyses (WGCNA) and gene mutation frequency analyses to screen for UV-related target genes using the GSE59455 and the cancer genome atlas databases (TCGA). The prognostic model was created using Cox regression and least absolute shrinkage and selection operator analyses (LASSCO). Furthermore, in vitro experiments further validated that the overexpression or knockdown of COL4A3 could regulate the proliferation and migration abilities of SKMEL28 and A357 melanoma cells.

Results

A prognostic model was created that included six genes with a high UV-related mutation in SKCM: COL4A3, CHRM2, DSC3, GIMAP5, LAMC2, and PSG7. The model had a strong patient survival correlation (P˂0.001, hazard ratio (HR) = 1.57) and significant predictor (P˂0.001, HR = 3.050). Furthermore, the model negatively correlated with immune cells, including CD8+ T cells (Cor=-0.408, P˂0.001), and M1-type macrophages (Cor=-0.385, P˂0.001), and immune checkpoints, including programmed cell death ligand-1. Moreover, we identified COL4A3 as a molecule with significant predictive functionality. Overexpression of COL4A3 significantly inhibited the proliferation, migration, and invasion abilities of SKMEL28 and A357 melanoma cells, while knockdown of COL4A3 yielded the opposite results. And overexpression of COL4A3 enhanced the inhibitory effects of imatinib on the proliferation, migration, and invasion abilities of SKMEL28 and A357 cells.

Conclusion

The efficacy of the prognostic model was validated by analyzing the prognosis, immune infiltration, and immune checkpoint profiles. COL4A3 stands out as a novel diagnostic and therapeutic target for SKCM, offering new strategies for small-molecule targeted drug therapies.

Single-cell dissection of the multicellular ecosystem and molecular features underlying microvascular invasion in HCC

BACKGROUND AND AIMS

Microvascular invasion (MVI) is a crucial pathological hallmark of HCC that is closely associated with poor outcomes, early recurrence, and intrahepatic metastasis following surgical resection and transplantation. However, the intricate tumor microenvironment and transcriptional programs underlying MVI in HCC remain poorly understood.

APPROACH AND RESULTS

We performed single-cell RNA sequencing of 46,789 individual cells from 10 samples of MVI+ (MVI present) and MVI- (MVI absent) patients with HCC. We conducted comprehensive and comparative analyses to characterize cellular and molecular features associated with MVI and validated key findings using external bulk, single-cell, and spatial transcriptomic datasets coupled with multiplex immunofluorescence assays. The comparison identified specific subtypes of immune and stromal cells critical to the formation of the immunosuppressive and pro-metastatic microenvironment in MVI+ tumors, including cycling T cells, lysosomal associated membrane protein 3+ dendritic cells, triggering receptor expressed on myeloid cells 2+ macrophages, myofibroblasts, and arterial i endothelial cells. MVI+ malignant cells are characterized by high proliferation rates, whereas MVI- malignant cells exhibit an inflammatory milieu. Additionally, we identified the midkine-dominated interaction between triggering receptor expressed on myeloid cells 2+ macrophages and malignant cells as a contributor to MVI formation and tumor progression. Notably, we unveiled a spatially co-located multicellular community exerting a dominant role in shaping the immunosuppressive microenvironment of MVI and correlating with unfavorable prognosis.

CONCLUSIONS

This study provides a comprehensive single-cell atlas of MVI in HCC, shedding light on the complex multicellular ecosystem and molecular features associated with MVI. These findings deepen our understanding of the underlying mechanisms driving MVI and provide valuable insights for improving clinical diagnosis and developing more effective treatment strategies.

Microphysiological systems as models for immunologically 'cold' tumors

The tumor microenvironment (TME) is a diverse milieu of cells including cancerous and non-cancerous cells such as fibroblasts, pericytes, endothelial cells and immune cells. The intricate cellular interactions within the TME hold a central role in shaping the dynamics of cancer progression, influencing pivotal aspects such as tumor initiation, growth, invasion, response to therapeutic interventions, and the emergence of drug resistance. In immunologically 'cold' tumors, the TME is marked by a scarcity of infiltrating immune cells, limited antigen presentation in the absence of potent immune-stimulating signals, and an abundance of immunosuppressive factors. While strategies targeting the TME as a therapeutic avenue in 'cold' tumors have emerged, there is a pressing need for novel approaches that faithfully replicate the complex cellular and non-cellular interactions in order to develop targeted therapies that can effectively stimulate immune responses and improve therapeutic outcomes in patients. Microfluidic devices offer distinct advantages over traditional in vitro 3D co-culture models and in vivo animal models, as they better recapitulate key characteristics of the TME and allow for precise, controlled insights into the dynamic interplay between various immune, stromal and cancerous cell types at any timepoint. This review aims to underscore the pivotal role of microfluidic systems in advancing our understanding of the TME and presents current microfluidic model systems that aim to dissect tumor-stromal, tumor-immune and immune-stromal cellular interactions in various 'cold' tumors. Understanding the intricacies of the TME in 'cold' tumors is crucial for devising effective targeted therapies to reinvigorate immune responses and overcome the challenges of current immunotherapy approaches.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

Prognostic and immunotherapeutic potential of regulatory T cell-associated signature in ovarian cancer

Tumour-induced immunosuppressive microenvironments facilitate oncogenesis, with regulatory T cells (Tregs) serving as a crucial component. The significance of Treg-associated genes within the context of ovarian cancer (OC) remains elucidated insufficiently. Utilizing single-cell RNA sequencing (scRNA-Seq) for the identification of Treg-specific biomarkers, this investigation employed single-sample gene set enrichment analysis (ssGSEA) for the derivation of a Treg signature score. Weighted gene co-expression network analysis (WGCNA) facilitated the identification of Treg-correlated genes. Machine learning algorithms were employed to determine an optimal prognostic model, subsequently exploring disparities across risk strata in terms of survival outcomes, immunological infiltration, pathway activation and responsiveness to immunotherapy. Through WGCNA, a cohort of 365 Treg-associated genes was discerned, with 70 implicated in the prognostication of OC. A Tregs-associated signature (TAS), synthesized from random survival forest (RSF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, exhibited robust predictive validity across both internal and external cohorts. Low TAS OC patients demonstrated superior survival outcomes, augmented by increased immunological cell infiltration, upregulated immune checkpoint expression, distinct pathway enrichment and differential response to immunotherapeutic interventions. The devised TAS proficiently prognosticates patient outcomes and delineates the immunological milieu within OC, offering a strategic instrument for the clinical stratification and selection of patients.

Spatial profiling reveals tissue-specific neuro-immune interactions in gastroenteropancreatic neuroendocrine tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are heterogeneous malignancies that arise from complex cellular interactions within the tissue microenvironment. Here, we sought to decipher tumor-derived signals from the surrounding microenvironment by applying digital spatial profiling (DSP) to hormone-secreting and non-functional GEP-NETs. By combining this approach with in vitro studies of human-derived organoids, we demonstrated the convergence of cell autonomous immune and pro-inflammatory proteins that suggests their role in neuroendocrine differentiation and tumorigenesis. DSP was used to evaluate the expression of 40 neural- and immune-related proteins in surgically resected duodenal and pancreatic NETs (n = 20) primarily consisting of gastrinomas (18/20). A total of 279 regions of interest were examined between tumors, adjacent normal and abnormal-appearing epithelium, and the surrounding stroma. The results were stratified by tissue type and multiple endocrine neoplasia I (MEN1) status, whereas protein expression was validated by immunohistochemistry (IHC). A tumor immune cell autonomous inflammatory signature was further evaluated by IHC and RNAscope, while functional pro-inflammatory signaling was confirmed using patient-derived duodenal organoids. Gastrin-secreting and non-functional pancreatic NETs showed a higher abundance of immune cell markers and immune infiltrate compared with duodenal gastrinomas. Compared with non-MEN1 tumors, MEN1 gastrinomas and preneoplastic lesions showed strong immune exclusion and upregulated expression of neuropathological proteins. Despite a paucity of immune cells, duodenal gastrinomas expressed the pro-inflammatory and pro-neural factor IL-17B. Treatment of human duodenal organoids with IL-17B activated NF-κB and STAT3 signaling and induced the expression of neuroendocrine markers. In conclusion, multiplexed spatial protein analysis identified tissue-specific neuro-immune signatures in GEP-NETs. Duodenal gastrinomas are characterized by an immunologically cold microenvironment that permits cellular reprogramming and neoplastic transformation of the preneoplastic epithelium. Moreover, duodenal gastrinomas cell autonomously express immune and pro-inflammatory factors, including tumor-derived IL-17B, that stimulate the neuroendocrine phenotype. © 2024 The Pathological Society of Great Britain and Ireland.

T-cell infiltration and its regulatory mechanisms in cancers: insights at single-cell resolution

Tumor-infiltrating T cells recognize, attack, and clear tumor cells, playing a central role in antitumor immune response. However, certain immune cells can impair this response and help tumor immune escape. Therefore, exploring the factors that influence T-cell infiltration is crucial to understand tumor immunity and improve therapeutic effect of cancer immunotherapy. The use of single-cell RNA sequencing (scRNA-seq) allows the high-resolution analysis of the precise composition of immune cells with different phenotypes and other microenvironmental factors, including non-immune stromal cells and the related molecules in the tumor microenvironment of various cancer types. In this review, we summarized the research progress on T-cell infiltration and the crosstalk of other stromal cells and cytokines during T-cell infiltration using scRNA-seq to provide insights into the mechanisms regulating T-cell infiltration and contribute new perspectives on tumor immunotherapy.

Single-cell transcriptome analysis of epithelial, immune, and stromal signatures and interactions in human ovarian cancer

A comprehensive investigation of ovarian cancer (OC) progression at the single-cell level is crucial for enhancing our understanding of the disease, as well as for the development of better diagnoses and treatments. Here, over half a million single-cell transcriptome data were collected from 84 OC patients across all clinical stages. Through integrative analysis, we identified heterogeneous epithelial-immune-stromal cellular compartments and their interactions in the OC microenvironment. The epithelial cells displayed clinical subtype features with functional variance. A significant increase in distinct T cell subtypes was identified including Tregs and CD8+ exhausted T cells from stage IC2. Additionally, we discovered antigen-presenting cancer-associated fibroblasts (CAFs), with myofibroblastic CAFs (myCAFs) exhibiting enriched extracellular matrix (ECM) functionality linked to tumor progression at stage IC2. Furthermore, the NECTIN2-TIGIT ligand-receptor pair was identified to mediate T cells communicating with epithelial, fibroblast, endothelial, and other cell types. Knock-out of NECTIN2 using CRISPR/Cas9 inhibited ovarian cancer cell (SKOV3) proliferation, and increased T cell proliferation when co-cultured. These findings shed light on the cellular compartments and functional aspects of OC, providing insights into the molecular mechanisms underlying stage IC2 and potential therapeutic strategies for OC.

Globally shared TCR repertoires within the tumor-infiltrating lymphocytes of patients with metastatic gynecologic cancer

Gynecologic cancer, including ovarian cancer and endometrial cancer, is characterized by morphological and molecular heterogeneity. Germline and somatic testing are available for patients to screen for pathogenic variants in genes such as BRCA1/2. Tissue expression levels of immunogenomic markers such as PD-L1 are also being used in clinical research. The basic therapeutic approach to gynecologic cancer combines surgery with chemotherapy. Immunotherapy, while not yet a mainstream treatment for gynecologic cancers, is advancing, with Dostarlimab recently receiving approval as a treatment for endometrial cancer. The goal remains to harness stimulated immune cells in the bloodstream to eradicate multiple metastases, a feat currently deemed challenging in a typical clinical setting. For the discovery of novel immunotherapy-based tumor targets, tumor-infiltrating lymphocytes (TILs) give a key insight on tumor-related immune activities by providing T cell receptor (TCR) sequences. Understanding the TCR repertoires of TILs in metastatic tissues and the circulation is important from an immunotherapy standpoint, as a subset of T cells in the blood have the potential to help kill tumor cells. To explore the relationship between distant tissue biopsy regions and blood circulation, we investigated the TCR beta chain (TCRβ) in bulk tumor and matched blood samples from 39 patients with gynecologic cancer. We found that the TCR clones of TILs at different tumor sites were globally shared within patients and had high overlap with the TCR clones in peripheral blood.

Deciphering tumor immune microenvironment differences between high-grade serous and endometrioid ovarian cancer to investigate their potential in indicating immunotherapy response

BACKGROUND

Ovarian cancer is a significant public health concern with a poor prognosis for epithelial ovarian cancer. To explore the potential of immunotherapy in treating epithelial ovarian cancer, we investigated the immune microenvironments of 52 patients with epithelial ovarian cancer, including 43 with high-grade serous ovarian cancer and 9 with endometrioid ovarian cancer.

RESULTS

Fresh tumor tissue was analyzed for genetic mutations and various parameters related to immune evasion and infiltration. The mean stromal score (stromal cell infiltration) in high-grade serous ovarian cancer was higher than in endometrioid ovarian cancer. The infiltration of CD8 T cells and exhausted CD8 T cells were found to be more extensive in high-grade serous ovarian cancer. Tumor Immune Dysfunction and Exclusion scores, T cell exclusion scores, and cancer-associated fibroblasts (CAF) scores were also higher in the high-grade serous ovarian cancer group, suggesting that the number of cytotoxic lymphocytes in the tumor microenvironment of high-grade serous ovarian cancer is likely lower compared to endometrioid ovarian cancer.

CONCLUSIONS

The high mean stromal score and more extensive infiltration and exhaustion of CD8 T cells in high-grade serous ovarian cancer indicate that high-grade serous ovarian cancer exhibits a higher level of cytotoxic T cell infiltration, yet these T cells tend to be in a dysfunctional state. Higher Tumor Immune Dysfunction and Exclusion scores, T cell exclusion scores, and CAF scores in high-grade serous ovarian cancers suggest that immune escape is more likely to occur in high-grade serous ovarian cancer, thus endometrioid ovarian cancer may be more conducive to immunotherapy. Therefore, it is crucial to design immunotherapy clinical trials for ovarian cancer to distinguish between high-grade serous and endometrioid ovarian cancer from the outset. This distinction will help optimize treatment strategies and improve outcomes for patients with different subtypes.

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

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

Multi-Omics Integration for the Design of Novel Therapies and the Identification of Novel Biomarkers

Multi-omics is a cutting-edge approach that combines data from different biomolecular levels, such as DNA, RNA, proteins, metabolites, and epigenetic marks, to obtain a holistic view of how living systems work and interact. Multi-omics has been used for various purposes in biomedical research, such as identifying new diseases, discovering new drugs, personalizing treatments, and optimizing therapies. This review summarizes the latest progress and challenges of multi-omics for designing new treatments for human diseases, focusing on how to integrate and analyze multiple proteome data and examples of how to use multi-proteomics data to identify new drug targets. We also discussed the future directions and opportunities of multi-omics for developing innovative and effective therapies by deciphering proteome complexity.

Overview of Tumor Heterogeneity in High-Grade Serous Ovarian Cancers

Ovarian cancers encompass a group of neoplasms originating from germinal tissues and exhibiting distinct clinical, pathological, and molecular features. Among these, epithelial ovarian cancers (EOCs) are the most prevalent, comprising five distinct tumor histotypes. Notably, high-grade serous ovarian cancers (HGSOCs) represent the majority, accounting for over 70% of EOC cases. Due to their silent and asymptomatic behavior, HGSOCs are generally diagnosed in advanced stages with an evolved and complex genomic state, characterized by high intratumor heterogeneity (ITH) due to chromosomal instability that distinguishes HGSOCs. Histologically, these cancers exhibit significant morphological diversity both within and between tumors. The histologic patterns associated with solid, endometrioid, and transitional (SET) and classic subtypes of HGSOCs offer prognostic insights and may indicate specific molecular profiles. The evolution of HGSOC from primary to metastasis is typically characterized by clonal ITH, involving shared or divergent mutations in neoplastic sub-clones within primary and metastatic sites. Disease progression and therapy resistance are also influenced by non-clonal ITH, related to interactions with the tumor microenvironment and further genomic changes. Notably, significant alterations occur in nonmalignant cells, including cancer-associated fibroblast and immune cells, during tumor progression. This review provides an overview of the complex nature of HGSOC, encompassing its various aspects of intratumor heterogeneity, histological patterns, and its dynamic evolution during progression and therapy resistance.

The nexus of dynamic T cell states and immune checkpoint blockade therapy in the periphery and tumor microenvironment

Immune checkpoint blockade (ICB) therapies, that is, using monoclonal antibodies to reinvigorate tumor-reactive, antigen-specific T cells from the inhibitory effects of CTLA-4, PD-1 and PD-L1 immune checkpoints, have revolutionized the therapeutic landscape of modern oncology. However, only a subset of patients can benefit from the ICB therapy. Biomarkers associated with ICB response, resistance and prognosis have been subjected to intensive research in the past decade. Early studies focused on the analysis of tumor specimens and their residing microenvironment. However, biopsies can be challenging to obtain in clinical practice, and do not reflect the dynamic changes of immunological parameters during the ICB therapy. Recent studies have investigated profiles of antigen-specific T cells derived from the peripheral compartment using multi-omics approaches. By tracking the clonotype and diversity of tumor-reactive T cell receptor repertoire, these studies collectively establish that de novo priming of antigen-specific T cells in peripheral blood occurs throughout the course of ICB, whereas preexisting T cells prior to ICB are exhausted to various degrees. Here, we review what is known about ICB-induced T cell phenotypic and functional changes in cancer patients both within the tumor microenvironment and in the peripheral compartment. A better understanding of parameters influencing the response to ICBs will provide rationales for developing novel diagnostics and combinatorial therapeutic strategies to maximize the clinical efficacies of ICB therapies.

High Tumor-Infiltrating Lymphocyte Count Is Associated with Distinct Gene Expression Profile and Longer Patient Survival in Advanced Ovarian Cancer

Cancer-related immunity plays a significant role in the outcome of ovarian cancer, but the exact mechanisms are not fully explored. A retrospective, real-life observational study was conducted including 57 advanced ovarian cancer patients. Immunohistochemistry for CD4+, CD8+, and CD45+ was used for assessing tumor-infiltrating immune cells. Furthermore, an immune-related gene expression assay was performed on 12-10 samples from patients with less than and more than 1-year overall survival (OS), respectively. A higher number of CD4+ (p = 0.0028) and CD45+ (p = 0.0221) immune cells within the tumor microenvironment were associated with longer OS of patients. In a multivariate setting, higher CD4+ T cell infiltration predicted longer OS (p = 0.0392). Twenty-three differentially expressed genes-involved in antigen presentation, costimulatory signaling, matrix remodeling, metastasis formation, and myeloid cell activity-were found when comparing the prognostic groups. It was found that tumor-infiltrating immune cell counts are associated with peculiar gene expression patterns and bear prognostic information in ovarian cancer. SOX11 expression emerged and was validated as a predictive marker for OS.

The diverse effects of transforming growth factor-β and SMAD signaling pathways during the CTL response

Cytotoxic T lymphocytes (CTLs) play an important role in defense against infections with intracellular pathogens and anti-tumor immunity. Efficient migration is required to locate and destroy infected cells in different regions of the body. CTLs accomplish this task by differentiating into specialized subsets of effector and memory CD8 T cells that traffic to different tissues. Transforming growth factor-beta (TGFβ) belongs to a large family of growth factors that elicit diverse cellular responses via canonical and non-canonical signaling pathways. Canonical SMAD-dependent signaling pathways are required to coordinate changes in homing receptor expression as CTLs traffic between different tissues. In this review, we discuss the various ways that TGFβ and SMAD-dependent signaling pathways shape the cellular immune response and transcriptional programming of newly activated CTLs. As protective immunity requires access to the circulation, emphasis is placed on cellular processes that are required for cell-migration through the vasculature.

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.