Integrative analysis deciphers the heterogeneity of cancer-associated fibroblast and implications on clinical outcomes in ovarian cancers

The unveiled mosaic of intra-tumor heterogeneity in ovarian cancer through spatial transcriptomic technologies: A systematic review

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

Heterogeneity of cancer-associated fibroblasts and tumor-promoting roles in head and neck squamous cell carcinoma

Tumor microenvironment (TME) in head and neck squamous cell carcinoma (HNSCC) has a major influence on disease progression and therapy response. One of the predominant stromal cell types in the TME of HNSCC is cancer-associated fibroblasts (CAF). CAF constitute a diverse cell population and we are only at the beginning of characterizing and understanding the functions of various CAF subsets. CAF have been shown to interact with tumor cells and other components of the TME to shape mainly a favourable microenvironment for HNSCC progression, although some studies report existence of tumor-restraining CAF subtypes. The numerous pathways used by CAF to promote tumorigenesis may represent potential therapeutic targets. This review summarizes current knowledge on the origins, subtypes and mechanisms employed by CAF in HNSCC. The aim is to contribute to the understanding on how CAF actively influence the TME and modulate different immune cell types, as well as cancer cells, to establish a conducive setting for cancer growth. Although CAF are currently a promising therapeutic target for the treatment of other types of cancer, there is no significant therapeutic advancement in HNSCC.

The β-arrestin1/endothelin axis bolsters ovarian fibroblast-dependent invadosome activity and cancer cell metastatic potential

Recruitment of fibroblasts to tumors and their activation into cancer-associated fibroblasts (CAFs) is a strategy used by tumor cells to direct extracellular matrix (ECM) remodeling, invasion, and metastasis, highlighting the need to investigate the molecular mechanisms driving CAF function. Endothelin-1 (ET-1) regulates the communication between cancer and stroma and facilitates the progression of serous ovarian cancer (SOC). By binding to Endothelin A (ETA) and B (ETB) receptors, ET-1 enables the recruitment of β-arrestin1 (β-arr1) and the formation of signaling complexes that coordinate tumor progression. However, how ET-1 receptors might "educate" human ovarian fibroblasts (HOFs) to produce altered ECM and promote metastasis remains to be elucidated. This study identifies ET-1 as a pivotal factor in the activation of CAFs capable of proteolytic ECM remodeling and the generation of heterotypic spheroids containing cancer cells with a propensity to metastasize. An autocrine/paracrine ET-1/ETA/BR/β-arr1 loop enhances HOF proliferation, upregulates CAF marker expression, secretes pro-inflammatory cytokines, and increases collagen contractility, and cell motility. Furthermore, ET-1 facilitates ECM remodeling by promoting the lytic activity of invadosome and activation of integrin β1. In addition, ET-1 signaling supports the formation of heterotypic HOF/SOC spheroids with enhanced ability to migrate through the mesothelial monolayer, and invade, representing metastatic units. The blockade of ETA/BR or β-arr1 silencing prevents CAF activation, invadosome function, mesothelial clearance, and the invasive ability of heterotypic spheroids. In vivo, therapeutic inhibition of ETA/BR using bosentan (BOS) significantly reduces the metastatic potential of combined HOFs/SOC cells, associated with enhanced apoptotic effects on tumor cells and stromal components. These findings support a model in which ET-1/β-arr1 reinforces tumor/stroma interaction through CAF activation and fosters the survival and metastatic properties of SOC cells, which could be counteracted by ETA/BR antagonists.

Exploring the diversity of cancer-associated fibroblasts: insights into mechanisms of drug resistance

Introduction: Among the various stromal cell types within the tumor microenvironment, cancer-associated fibroblasts (CAFs) emerge as the predominant constituent, exhibiting a diverse array of oncogenic functions not intrinsic to normal fibroblasts. Their involvement spans across all stages of tumorigenesis, encompassing initiation, progression, and metastasis. Current understanding posits the coexistence of distinct subpopulations of CAFs within the tumor microenvironment across a spectrum of solid tumors, showcasing both pro- and antitumor activities. Recent advancements in single-cell transcriptomics have revolutionized our ability to meticulously dissect the heterogeneity inherent to CAF populations. Furthermore, accumulating evidence underscores the pivotal role of CAFs in conferring therapeutic resistance to tumors against various drug modalities. Consequently, efforts are underway to develop pharmacological agents specifically targeting CAFs. Methods: This review embarks on a comprehensive analysis, consolidating data from 36 independent single-cell RNA sequencing investigations spanning 17 distinct human malignant tumor types. Results: Our exploration centers on elucidating CAF population markers, discerning their prognostic relevance, delineating their functional contributions, and elucidating the underlying mechanisms orchestrating chemoresistance. Discussion: Finally, we deliberate on the therapeutic potential of harnessing CAFs as promising targets for intervention strategies in clinical oncology.

The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low-risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI-207 show a greater efficacy in patients in the low-risk category. Conversely, for the high-risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision-making for HCC patients.

Integrative analysis identifies cancer cell-intrinsic RARRES1 as a predictor of prognosis and immune response in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with poor prognosis and limited treatment options. Although immune checkpoint inhibitors (ICIs) have been proven to improve outcomes in TNBC patients, the potential mechanisms and markers that determine the therapeutic response to ICIs remains uncertain. Revealing the relationship and interaction between cancer cells and tumor microenvironment (TME) could be helpful in predicting treatment efficacy and developing novel therapeutic agents. By analyzing single-cell RNA sequencing dataset, we comprehensively profiled cell types and subpopulations as well as identified their signatures in the TME of TNBC. We also proposed a method for quantitatively assessment of the TME immune profile and provided a framework for identifying cancer cell-intrinsic features associated with TME through integrated analysis. Using integrative analyses, RARRES1 was identified as a TME-associated gene, whose expression was positively correlated with prognosis and response to ICIs in TNBC. In conclusion, this study characterized the heterogeneity of cellular components in TME of TNBC patients, and brought new insights into the relationship between cancer cells and TME. In addition, RARRES1 was identified as a potential predictor of prognosis and response to ICIs in TNBC.

Fibroblast heterogeneity and functions: insights from single-cell sequencing in wound healing, breast cancer, ovarian cancer and melanoma

Cancer has been described as the wound that does not heal, in large part due to fibroblast involvement. Activation of cancer-associated fibroblasts (CAFs) contributes to critical features of the tumor microenvironment, including upregulation of key marker proteins, recruitment of immune cells, and deposition of extracellular matrix (ECM)-similar to fibroblast activation in injury-induced wound healing. Prior to the widespread availability of single-cell RNA sequencing (scRNA seq), studies of CAFs or fibroblasts in wound healing largely relied on models guided by individual fibroblast markers, or methods with less resolution to unravel the heterogeneous nature of CAFs and wound healing fibroblasts (especially regarding scarring outcome). Here, insights from the enhanced resolution provided by scRNA sequencing of fibroblasts in normal wound healing, breast cancer, ovarian cancer, and melanoma are discussed. These data have revealed differences in expression of established canonical activation marker genes, epigenetic modifications, fibroblast lineages, new gene and proteins of clinical interest for further experimentation, and novel signaling interactions with other cell types that include spatial information.

Identification of a cancer associated fibroblasts-related index to predict prognosis and immune landscape in ovarian cancer

Cancer-associated fibroblasts (CAFs) play a role in ovarian cancer (OV) evolution, immunosuppression and promotion of drug resistance. Exploring the value of CAFs-related biomarker in OV is of great importance. In the present work, we developed a CAFs-related index (CAFRI) based on an integrated analysis of single-cell and bulk RNA-sequencing and highlighted the value of CAFRI in predicting clinical outcomes in individuals with OV, tumour immune microenvironment (TIME) and response to immune checkpoint inhibitors (ICIs). The GSE151214 cohort was used for cell subpopulation localization and analysis, the TCGA-OV patients as a training set. Moreover, the ICGC-OV, GSE26193, GSE26712 and GSE19829 cohorts were used for the validation of CAFRI. The TIMER 2.0, CIBERSORT and ssGSEA algorithms were used for analysis of TIME characteristics based on the CAFRI. The GSVA, GSEA, GO, KEGG and tumour mutation burden (TMB) analyses were used for mechanistic exploration. Additionally, the IMvigor210 cohort was conducted to validate the predictive value of CAFRI on the efficacy of ICIs. Finally, CAFRI-based antitumour drug sensitivity was analysed. The findings demonstrate that the CAFRI can served as an excellent predictor of prognosis for individuals with OV, as well as identifying patients with different TIME characteristics, differentiating between immune 'hot' and 'cold' tumour populations, and providing new insights into the selection of ICIs and personalised treatment regimens. CAFRI provides new perspectives for the development of novel prognostic and immunotherapy efficacy predictive biomarkers for OV.

Define cancer-associated fibroblasts (CAFs) in the tumor microenvironment: new opportunities in cancer immunotherapy and advances in clinical trials

Despite centuries since the discovery and study of cancer, cancer is still a lethal and intractable health issue worldwide. Cancer-associated fibroblasts (CAFs) have gained much attention as a pivotal component of the tumor microenvironment. The versatility and sophisticated mechanisms of CAFs in facilitating cancer progression have been elucidated extensively, including promoting cancer angiogenesis and metastasis, inducing drug resistance, reshaping the extracellular matrix, and developing an immunosuppressive microenvironment. Owing to their robust tumor-promoting function, CAFs are considered a promising target for oncotherapy. However, CAFs are a highly heterogeneous group of cells. Some subpopulations exert an inhibitory role in tumor growth, which implies that CAF-targeting approaches must be more precise and individualized. This review comprehensively summarize the origin, phenotypical, and functional heterogeneity of CAFs. More importantly, we underscore advances in strategies and clinical trials to target CAF in various cancers, and we also summarize progressions of CAF in cancer immunotherapy.

Insights into the tumor-stromal-immune cell metabolism cross talk in ovarian cancer

The ovarian cancer tumor microenvironment (TME) consists of a constellation of abundant cellular components, extracellular matrix, and soluble factors. Soluble factors, such as cytokines, chemokines, structural proteins, extracellular vesicles, and metabolites, are critical means of noncontact cellular communication acting as messengers to convey pro- or antitumorigenic signals. Vast advancements have been made in our understanding of how cancer cells adapt their metabolism to meet environmental demands and utilize these adaptations to promote survival, metastasis, and therapeutic resistance. The stromal TME contribution to this metabolic rewiring has been relatively underexplored, particularly in ovarian cancer. Thus, metabolic activity alterations in the TME hold promise for further study and potential therapeutic exploitation. In this review, we focus on the cellular components of the TME with emphasis on 1) metabolic signatures of ovarian cancer; 2) understanding the stromal cell network and their metabolic cross talk with tumor cells; and 3) how stromal and tumor cell metabolites alter intratumoral immune cell metabolism and function. Together, these elements provide insight into the metabolic influence of the TME and emphasize the importance of understanding how metabolic performance drives cancer progression.

Identifying ITGB2 as a Potential Prognostic Biomarker in Ovarian Cancer

Epithelial ovarian cancer is by far the most lethal gynecological malignancy. The exploration of promising immunomarkers to predict prognosis in ovarian cancer patients remains challenging. In our research, we carried out an integrated bioinformatic analysis of genome expressions and their immune characteristics in the ovarian cancer microenvironment with validation in different experiments. We filtrated 332 differentially expressed genes with 10 upregulated hub genes from the Gene Expression Omnibus database. These genes were closely related to ovarian tumorigenesis. Subsequently, the survival and immune infiltration analysis demonstrated that the upregulation of five candidate genes, ITGB2, VEGFA, CLDN4, OCLN, and SPP1, were correlated with an unfavorable clinical outcome and increased immune cell infiltration in ovarian cancer. Of these genes, ITGB2 tended to be the gene most correlated with various immune cell infiltrations and had a strong correlation with significant M2 macrophages infiltration (r = 0.707, p = 4.71 × 10^-39), while it had a moderate correlation with CD4+/CD8+ T cells and B cells. This characteristic explains why the high expression of ITGB2 was accompanied by immune activation but did not reverse carcinogenesis. Additionally, we confirmed that ITGB2 was over-expressed in ovarian cancer tissues and was mainly located in cytoplasm, detected by Western blotting and the immunohistochemical method. In summary, ITGB2 may serve as a prognostic immunomarker for ovarian cancer patients.