Stromal Protein-Mediated Immune Regulation in Digestive Cancers

Role and value of the tumor microenvironment in the progression and treatment resistance of gastric cancer (Review)

Gastric cancer (GC) is characterized by a complex and heterogeneous tumor microenvironment (TME) that significantly influences disease progression and treatment outcomes. The tumor stroma, which is composed of a variety of cell types such as cancer‑associated fibroblasts, immune cells and vascular components, displays significant spatial and temporal diversity. These stromal elements engage in dynamic crosstalk with cancer cells, shaping their proliferative, invasive and metastatic potential. Furthermore, the TME is instrumental in facilitating resistance to traditional chemotherapy, specific treatments and immunotherapy strategies. Understanding the underlying mechanisms by which the GC microenvironment evolves and supports tumor growth and therapeutic resistance is critical for developing effective treatment strategies. The present review explores the latest progress in understanding the intricate interactions between cancer cells and their immediate environment in GC, highlighting the implications for disease pathogenesis and therapeutic interventions.

Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer

BACKGROUND

Breast cancer (BC), a leading malignant disease, affects women all over the world. Cancer associated fibroblasts (CAFs) stimulate epithelial-mesenchymal transition, and induce chemoresistance and immunosuppression.

AIM

To establish a CAFs-associated prognostic signature to improve BC patient outcome estimation.

METHODS

We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas (TCGA) databases, and 3661 BC samples from the The Gene Expression Omnibus. CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm. CAF-associated gene identification was done by weighted gene co-expression network analysis. A CAF risk signature was established via univariate, least absolute shrinkage and selection operator regression, and multivariate Cox regression analyses. The receiver operating characteristic (ROC) and Kaplan-Meier curves were employed to evaluate the predictability of the model. Subsequently, a nomogram was developed with the risk score and patient clinical signature. Using Spearman's correlations analysis, the relationship between CAF risk score and gene set enrichment scores were examined. Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

Employing an 8-gene (IL18, MYD88, GLIPR1, TNN, BHLHE41, DNAJB5, FKBP14, and XG) signature, we attempted to estimate BC patient prognosis. Based on our analysis, high-risk patients exhibited worse outcomes than low-risk patients. Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis. ROC analysis exhibited satisfactory nomogram predictability. The area under the curve showed 0.805 at 3 years, and 0.801 at 5 years in the TCGA cohort. We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes. CAF risk score was significantly correlated with most hallmark gene sets. Finally, the prognostic signature were further validated by qRT-PCR.

CONCLUSION

We introduced a newly-discovered CAFs-associated gene signature, which can be employed to estimate BC patient outcomes conveniently and accurately.

Stiffness-induced cancer-associated fibroblasts are responsible for immunosuppression in a platelet-derived growth factor ligand-dependent manner

Pancreatic ductal adenocarcinoma (PDAC) is associated with a vast stromal reaction that arises mainly from cancer-associated fibroblasts (CAFs) and promotes both immune escape and tumor growth. Here, we used a mouse model with deletion of the activin A receptor ALK4 in the context of the KrasG12D mutation, which strongly drives collagen deposition that leads to tissue stiffness. By ligand-receptor analysis of single-cell RNA-sequencing data, we identified that, in stiff conditions, neoplastic ductal cells instructed CAFs through sustained platelet-derived growth factor (PDGF) signaling. Tumor-associated tissue rigidity resulted in the emergence of stiffness-induced CAFs (siCAFs) in vitro and in vivo. Similar results were confirmed in human data. siCAFs were able to strongly inhibit CD8+ T-cell responses in vitro and in vivo, promoting local immunosuppression. More importantly, targeting PDGF signaling led to diminished siCAF and reduced tumor growth. Our data show for the first time that early paracrine signaling leads to profound changes in tissue mechanics, impacting immune responses and tumor progression. Our study highlights that PDGF ligand neutralization can normalize the tissue architecture independent of the genetic background, indicating that finely tuned stromal therapy may open new therapeutic avenues in pancreatic cancer.

Glucose oxidase virus-based nanoreactors for smart breast cancer therapy

BACKGROUND

Breast cancer is the most common malignant tumor disease and the leading cause of female mortality. The evolution of nanomaterials science opens the opportunity to improve traditional cancer therapies, enhancing therapy efficiency and reducing side effects.

METHODS AND MAJOR RESULTS

Herein, protein cages conceived as enzymatic nanoreactors were designed and produced by using virus-like nanoparticles (VLPs) from Brome mosaic virus (BMV) and containing the catalytic activity of glucose oxidase (GOx) enzyme. The GOx enzyme was encapsulated into the BMV capsid (VLP-GOx), and the resulting enzymatic nanoreactors were coated with human serum albumin (VLP-GOx@HSA) for breast tumor cell targeting. The effect of the synthesized GOx nanoreactors on breast tumor cell lines was studied in vitro. Both nanoreactor preparations VLP-GOx and VLP-GOx@HSA showed to be highly cytotoxic for breast tumor cell cultures. Cytotoxicity for human embryonic kidney cells was also found. The monitoring of nanoreactor treatment on triple-negative breast cancer cells showed an evident production of oxygen by the catalase antioxidant enzyme induced by the high production of hydrogen peroxide from GOx activity.

CONCLUSIONS AND IMPLICATIONS

The nanoreactors containing GOx activity are entirely suitable for cytotoxicity generation in tumor cells. The HSA functionalization of the VLP-GOx nanoreactors, a strategy designed for selective cancer targeting, showed no improvement in the cytotoxic effect. The GOx containing enzymatic nanoreactors seems to be an interesting alternative to improve the current cancer therapy. In vivo studies are ongoing to reinforce the effectiveness of this treatment strategy.

Cancer-associated fibroblasts refine the classifications of gastric cancer with distinct prognosis and tumor microenvironment characteristics

Background

Cancer-associated fibroblasts (CAFs) are essential tumoral components of gastric cancer (GC), contributing to the development, therapeutic resistance and immune-suppressive tumor microenvironment (TME) of GC. This study aimed to explore the factors related to matrix CAFs and establish a CAF model to evaluate the prognosis and therapeutic effect of GC.

Methods

Sample information from the multiply public databases were retrieved. Weighted gene co-expression network analysis was used to identify CAF-related genes. EPIC algorithm was used to construct and verify the model. Machine-learning methods characterized CAF risk. Gene set enrichment analysis was employed to elucidate the underlying mechanism of CAF in the development of GC.

Results

A three-gene (GLT8D2, SPARC and VCAN) prognostic CAF model was established, and patients were markedly divided according to the riskscore of CAF model. The high-risk CAF clusters had significantly worse prognoses and less significant responses to immunotherapy than the low-risk group. Additionally, the CAF risk score was positively associated with CAF infiltration in GC. Moreover, the expression of the three model biomarkers were significantly associated with the CAF infiltration. GSEA revealed significant enrichment of cell adhesion molecules, extracellular matrix receptors and focal adhesions in patients at a high risk of CAF.

Conclusion

The CAF signature refines the classifications of GC with distinct prognosis and clinicopathological indicators. The three-gene model could effectively aid in determining the prognosis, drug resistance and immunotherapy efficacy of GC. Thus, this model has promising clinical significance for guiding precise GC anti-CAF therapy combined with immunotherapy.

Integrated analysis of genome-wide DNA methylation and cancer-associated fibroblasts identified prognostic biomarkers and immune checkpoint blockade in lower grade gliomas

Background

Cancer-associated fibroblasts (CAFs) are vital components of prominent cellular components in lower-grade gliomas (LGGs) that contribute to LGGs' progression, treatment resistance, and immunosuppression. Epigenetic modification and immunity have significant implications for tumorigenesis and development.

Methods

We combined aberrant methylation and CAFs abundances to build a prognostic model and the impact on the biological properties of LGGs. Grouping based on the median CAFs abundances score of samples in the TCGA-LGGs dataset, differentially expressed genes and aberrantly methylated genes were combined for subsequent analysis.

Results

We identified five differentially methylated and expressed genes (LAT32, SWAP70, GSAP, EMP3, and SLC2A10) and established a prognostic gene signature validated in the CGGA-LGGs dataset. Immunohistochemistry (IHC) and in vitro tests were performed to verify these expressions. The high-risk group increased in tumor-promoting immune cells and tumor mutational burden. Notably, risk stratification had different ICB sensitivities in LGGs, and there were also significant sensitivity differences for temozolomide and the other three novel chemotherapeutic agents.

Conclusion

Our study reveals characteristics of CAFs in LGGs, refines the direct link between epigenetics and tumor stroma, and might provide clinical implications for guiding tailored anti-CAFs therapy in combination with immunotherapy for LGGs patients.

Cancer-Associated Fibroblast Risk Model for Prediction of Colorectal Carcinoma Prognosis and Therapeutic Responses

Colorectal carcinoma (CRC) is a malignant tumor of the digestive system. Cancer-associated fibroblasts (CAFs) are important cellular elements in the tumor microenvironment of CRC, which contribute to CRC progression and immune escape. To predict the survival outcome and therapeutic responses of CRC patients, we identified genes connected with stromal CAF and generated a risk model. In this study, we used multiple algorithms to reveal CAF-related genes in the Gene Expression Omnibus and The Cancer Genome Atlas datasets and construct a risk model composed by prognostic CAF-associated genes. Then, we evaluated whether the risk score could predict CAF infiltrations and immunotherapy in CRC and confirmed the expression of the risk model in CAFs. Our results showed that CRC patients with high CAF infiltrations and stromal score had worse prognosis than those with low-CAF infiltrations and stromal score. We obtained 88 stromal CAF-associated hub-genes and generated a CAF risk model consisting of ZNF532 and COLEC12. Compared with low-risk group, the overall survival in high-risk group was shorter. The relationship between risk score, ZNF532 and COLEC12, and stromal CAF infiltrations and CAF markers was positive. In addition, the effect of immunotherapy in the high-risk group was not as good as that in the low-risk group. Patients with the high-risk group were enriched in chemokine signaling pathway, cytokine-cytokine receptor interaction, and focal adhesion. Finally, we confirmed that the expressions of ZNF532 and COLEC12 in risk model were widely distributed in fibroblasts of CRC, and the expression levels were higher in fibroblasts than CRC cells. In conclusion, the prognostic CAF signature of ZNF532 and COLEC12 can be applied not only to predict the prognosis of CRC patients but also to evaluate the immunotherapy response in CRC patients, and these findings provide the possibility for further development of individualized treatment for CRC.

Nanoparticle-Based Drug Delivery Systems Targeting Tumor Microenvironment for Cancer Immunotherapy Resistance: Current Advances and Applications

Cancer immunotherapy has shown impressive anti-tumor activity in patients with advanced and early-stage malignant tumors, thus improving long-term survival. However, current cancer immunotherapy is limited by barriers such as low tumor specificity, poor response rate, and systemic toxicities, which result in the development of primary, adaptive, or acquired resistance. Immunotherapy resistance has complex mechanisms that depend on the interaction between tumor cells and the tumor microenvironment (TME). Therefore, targeting TME has recently received attention as a feasibility strategy for re-sensitizing resistant neoplastic niches to existing cancer immunotherapy. With the development of nanotechnology, nanoplatforms possess outstanding features, including high loading capacity, tunable porosity, and specific targeting to the desired locus. Therefore, nanoplatforms can significantly improve the effectiveness of immunotherapy while reducing its toxic and side effects on non-target cells that receive intense attention in cancer immunotherapy. This review explores the mechanisms of tumor microenvironment reprogramming in immunotherapy resistance, including TAMs, CAFs, vasculature, and hypoxia. We also examined whether the application of nano-drugs combined with current regimens is improving immunotherapy clinical outcomes in solid tumors.

SNAI1-expressing fibroblasts and derived-extracellular matrix as mediators of drug resistance in colorectal cancer patients

Resistance to antitumor treatments is one of the most important problems faced by clinicians in the management of colorectal cancer (CRC) patients. Cancer-Associated Fibroblasts (CAFs) are the main producers and remodelers of the extracellular matrix (ECM), which is directly involved in drug resistance mechanisms. Primary Normal Fibroblasts (NFs) and CAFs and cell lines (fibroblasts and tumor cells), were used to generate ECM and to identify its role in the oxaliplatin and cetuximab chemoresistance processes of CRC cells mediated by SNAI1-expressing fibroblasts. Matrices generated by Snai1 KO MEFs (Knockout Mouse Embryonic Fibroblasts) confer less resistance on oxaliplatin and cetuximab than wild-type MEF-derived matrices. Similarly, matrices derived from CAFs cause greater survival of colorectal cancer cells than NF-derived matrices, in a similar way to Snai1 expression levels. In addition, Snail1 expression in fibroblasts regulates drug resistance and metabolism gene expression in tumor cells mediated by ECM. Finally, a series of 531 patients (TCGA) with CRC was used to assess the role of SNAI1 expression in patients' prognosis indicating an association between tumor SNAI1 expression and overall survival in colon cancer patients but not in rectal cancer patients. SNAI1 expression in CRC cancer patients, together with in vitro experimentation, suggests the possible use of SNAI1 expression in tumor-associated fibroblasts as a predictive biomarker of response to oxaliplatin and cetuximab treatments in patients with CRC.

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

Cancer-associated fibroblasts (CAFs) are a major contributor to tumor stromal crosstalk in the tumor microenvironment (TME) and boost tumor progression by promoting angiogenesis and lymphangiogenesis. This study aimed to identify prognostic genes associated with CAFs that lead to high morbidity and mortality in ovarian cancer (OC) patients. We performed bioinformatics analysis in 16 multicenter studies (2,742 patients) and identified CAF-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify COL16A1, COL5A2, GREM1, LUM, SRPX, and TIMP3 and construct a prognostic signature. Subsequently, a series of bioinformatics algorithms indicated risk stratification based on the above signature, suggesting that high-risk patients have a worse prognosis, weaker immune response, and lower tumor mutational burden (TMB) status but may be more sensitive to routine chemotherapeutic agents. Finally, we characterized prognostic markers using cell lines, immunohistochemistry, and single-cell sequencing. In conclusion, these results suggest that the CAF-related signature may be a novel pretreatment guide for anti-CAFs, and prognostic markers in CAFs may be potential therapeutic targets to inhibit OC progression.

A Promising Biomarker and Therapeutic Target in Patients with Advanced PDAC: The Stromal Protein βig-h3

With an overall survival rate of 2–9% at 5 years, pancreatic ductal adenocarcinoma (PDAC) is currently the fourth leading cause of cancer-related deaths in the industrialized world and is predicted to become the second by 2030. Owing to often late diagnosis and rare actionable molecular alterations, PDAC has not yet benefited from the recent therapeutic advances that immune checkpoint inhibitors (ICI) have provided in other cancer types, except in specific subgroups of patients presenting with tumors with high mutational burden (TMB) or microsatellite instability (MSI). The tumor microenvironment (TME) plays a substantial role in therapeutic resistance by facilitating immune evasion. An extracellular stromal protein, βig-h3/TGFβi, is involved in the pathogenesis of PDAC by hampering T cell activation and promoting stiffness of the TME. The study BIGHPANC included 41 patients with metastatic PDAC, and analyzed βig-h3 levels in serum and tumor samples to assess the βig-h3 prognostic value. βig-h3 serum levels are significantly associated with overall survival (HR 2.05, 95%CI 1.07–3.93; p = 0.0301). Our results suggest that βig-h3 serum levels may be considered a prognostic biomarker in patients with metastatic PDAC.

Identification of prognostic biomarkers associated with tumor microenvironment in ceRNA network for esophageal squamous cell carcinoma: a bioinformatics study based on TCGA database

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer in the world with high incidence rate and poor prognosis. Infiltrated immune and stromal cells are vital components of tumor microenvironment (TME) and have a significant impact on the progression of ESCC. The competitive endogenous RNA (ceRNA) hypothesis has been proved important in the molecular biological mechanisms of tumor development. However, there are few studies on the relationship between ceRNA and ESCC TME.

METHODS

The proportion of tumor-infiltrating immune cells and the amount of stromal and immune cells in ESCC cases were calculated from The Cancer Genome Atlas database using the CIBERSORT and ESTIMATE calculation methods. After stratified identification of differentially expressed genes, WGCNA and miRNA prediction system were applied to construct ceRNA network. Finally, PPI network and survival analysis were selected to discriminate prognostic signature. And the results were verified in two independent groups from Gene Expression Omnibus and Lanzhou, China.

RESULTS

We found that high Stromal and ESTIMATE scores were significantly associated with poor overall survival. Three TME-related key prognostic genes were screened, namely, LCP2, CD86, SLA. And the expression of them was significantly correlated with infiltrated immunocytes. It is also found that ESTIMATE Score and the expression of CD86 were both related to TNM system of ESCC.

CONCLUSIONS

We identified three novel TME-related prognostic markers and their lncRNA-miRNA-mRNA pathway in ESCC patients, which may provide new strategies for the targeted therapy.

Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblast Signature for Predicting Prognosis and Therapeutic Responses in Gastric Cancer

Background: Cancer-associated fibroblasts (CAFs) are the most prominent cellular components in gastric cancer (GC) stroma that contribute to GC progression, treatment resistance, and immunosuppression. This study aimed at exploring stromal CAF-related factors and developing a CAF-related classifier for predicting prognosis and therapeutic effects in GC.

Methods: We downloaded mRNA expression and clinical information of 431 GC samples from Gene Expression Omnibus (GEO) and 330 GC samples from The Cancer Genome Atlas (TCGA) databases. CAF infiltrations were quantified by the estimate the proportion of immune and cancer cells (EPIC) method, and stromal scores were calculated via the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Stromal CAF-related genes were identified by weighted gene co-expression network analysis (WGCNA). A CAF risk signature was then developed using the univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. We applied the Spearman test to determine the correlation among CAF risk score, CAF markers, and CAF infiltrations (estimated via EPIC, xCell, microenvironment cell populations-counter (MCP-counter), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms). The TIDE algorithm was further used to assess immunotherapy response. Gene set enrichment analysis (GSEA) was applied to clarify the molecular mechanisms.

Results: The 4-gene (COL8A1, SPOCK1, AEBP1, and TIMP2) prognostic CAF model was constructed. GC patients were classified into high– and low–CAF-risk groups in accordance with their median CAF risk score, and patients in the high–CAF-risk group had significant worse prognosis. Spearman correlation analyses revealed the CAF risk score was strongly and positively correlated with stromal and CAF infiltrations, and the four model genes also exhibited positive correlations with CAF markers. Furthermore, TIDE analysis revealed high–CAF-risk patients were less likely to respond to immunotherapy. GSEA revealed that epithelial–mesenchymal transition (EMT), TGF-β signaling, hypoxia, and angiogenesis gene sets were significantly enriched in high–CAF-risk group patients.

Conclusion: The present four-gene prognostic CAF signature was not only reliable for predicting prognosis but also competent to estimate clinical immunotherapy response for GC patients, which might provide significant clinical implications for guiding tailored anti-CAF therapy in combination with immunotherapy for GC patients.

Tumor-Associated Macrophages and Inflammatory Microenvironment in Gastric Cancer: Novel Translational Implications

Gastric cancer (GC) represents the fifth most frequently diagnosed cancer worldwide, with a poor prognosis in patients with advanced disease despite many improvements in systemic treatments in the last decade. In fact, GC has shown resistance to several treatment options, and thus, notable efforts have been focused on the research and identification of novel therapeutic targets in this setting. The tumor microenvironment (TME) has emerged as a potential therapeutic target in several malignancies including GC, due to its pivotal role in cancer progression and drug resistance. Therefore, several agents and therapeutic strategies targeting the TME are currently under assessment in both preclinical and clinical studies. The present study provides an overview of available evidence of the inflammatory TME in GC, highlighting different types of tumor-associated cells and implications for future therapeutic strategies.

Adaptive Mechanisms of Tumor Therapy Resistance Driven by Tumor Microenvironment

Cancer is a disease which frequently has a poor prognosis. Although multiple therapeutic strategies have been developed for various cancers, including chemotherapy, radiotherapy, and immunotherapy, resistance to these treatments frequently impedes the clinical outcomes. Besides the active resistance driven by genetic and epigenetic alterations in tumor cells, the tumor microenvironment (TME) has also been reported to be a crucial regulator in tumorigenesis, progression, and resistance. Here, we propose that the adaptive mechanisms of tumor resistance are closely connected with the TME rather than depending on non-cell-autonomous changes in response to clinical treatment. Although the comprehensive understanding of adaptive mechanisms driven by the TME need further investigation to fully elucidate the mechanisms of tumor therapeutic resistance, many clinical treatments targeting the TME have been successful. In this review, we report on recent advances concerning the molecular events and important factors involved in the TME, particularly focusing on the contributions of the TME to adaptive resistance, and provide insights into potential therapeutic methods or translational medicine targeting the TME to overcome resistance to therapy in clinical treatment.