Establishing prognostic models for intrahepatic cholangiocarcinoma based on immune cells.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a malignant liver tumor that is challenging to treat and manage and current prognostic models for the disease are inefficient or ineffective. Tumor-associated immune cells are critical for tumor development and progression. The main goal of this study was to establish models based on tumor-associated immune cells for predicting the overall survival of patients undergoing surgery for ICC. AIM: To establish 1-year and 3-year prognostic models for ICC after surgical resection. METHODS: Immunohistochemical staining was performed for CD4, CD8, CD20, pan-cytokeratin (CK), and CD68 in tumors and paired adjacent tissues from 141 patients with ICC who underwent curative surgery. Selection of variables was based on regression diagnostic procedures and goodness-of-fit tests (PH assumption). Clinical parameters and pathological diagnoses, combined with the distribution of immune cells in tumors and paired adjacent tissues, were utilized to establish 1- and 3-year prognostic models. RESULTS: This is an important application of immune cells in the tumor microenvironment. CD4, CD8, CD20, and CK were included in the establishment of our prognostic model by stepwise selection, whereas CD68 was not significantly associated with the prognosis of ICC. By integrating clinical data associated with ICC, distinct prognostic models were derived for 1- and 3-year survival outcomes using variable selection. The 1-year prediction model yielded a C-index of 0.76 95% confidence interval (95%CI): 0.65-0.87 and the 3-year prediction model produced a C-index of 0.69 (95%CI: 0.65-0.73). Internal validation yielded a C-index of 0.761 (95%CI: 0.669-0.853) for the 1-year model and 0.693 (95%CI: 0.642-0.744) for the 3-year model. CONCLUSION: We developed Cox regression models for 1-year and 3-year survival predictions of patients with ICC who underwent resection, which has positive implications for establishing a more comprehensive prognostic model for ICC based on tumor immune microenvironment and immune cell changes in the future.

Mapping the function of MicroRNAs as a critical regulator of tumor-immune cell communication in breast cancer and potential treatment strategies.

Among women, breast cancer ranks as the most prevalent form of cancer, and the presence of metastases significantly reduces prognosis and diminishes overall survival rates. Gaining insights into the biological mechanisms governing the conversion of cancer cells, their subsequent spread to other areas of the body, and the immune system's monitoring of tumor growth will contribute to the advancement of more efficient and targeted therapies. MicroRNAs (miRNAs) play a critical role in the interaction between tumor cells and immune cells, facilitating tumor cells' evasion of the immune system and promoting cancer progression. Additionally, miRNAs also influence metastasis formation, including the establishment of metastatic sites and the transformation of tumor cells into migratory phenotypes. Specifically, dysregulated expression of these genes has been associated with abnormal expression of oncogenes and tumor suppressor genes, thereby facilitating tumor development. This study aims to provide a concise overview of the significance and function of miRNAs in breast cancer, focusing on their involvement as tumor suppressors in the antitumor immune response and as oncogenes in metastasis formation. Furthermore, miRNAs hold tremendous potential as targets for gene therapy due to their ability to modulate specific pathways that can either promote or suppress carcinogenesis. This perspective highlights the latest strategies developed for miRNA-based therapies.

Transglutaminases are oncogenic biomarkers in human cancers and therapeutic targeting of TGM2 blocks chemoresistance and macrophage infiltration in pancreatic cancer.

PURPOSE: Transglutaminases (TGs) are multifunctional enzymes exhibiting transglutaminase crosslinking, as well as atypical GTPase/ATPase and kinase activities. Here, we used an integrated comprehensive analysis to assess the genomic, transcriptomic and immunological landscapes of TGs across cancers. METHODS: Gene expression and immune cell infiltration patterns across cancers were obtained from The Cancer Genome Atlas (TCGA) database and Gene Set Enrichment Analysis (GSEA) datasets. Western blotting, immunofluorescence staining, enzyme-linked immunosorbent assays, and orthotopic xenograft models were used to validate our database-derived results. RESULTS: We found that the overall expression of TGs (designated as the TG score) is significantly upregulated in multiple cancers and related to a worse patient survival. The expression of TG family members can be regulated through multiple mechanisms at the genetic, epigenetic and transcriptional levels. The expression of transcription factors crucial for epithelial-to-mesenchymal transition (EMT) is commonly correlated with the TG score in many cancer types. Importantly, TGM2 expression displays a close connection with chemoresistance to a wide range of chemotherapeutic drugs. We found that TGM2 expression, F13A1 expression and the overall TG score were positively correlated with the infiltration of immune cells in all cancer types tested. Functional and clinical verification revealed that a higher TGM2 expression is linked with a worse patient survival, an increased IC50 value of gemcitabine, and a higher abundance of tumor-infiltrating macrophages in pancreatic cancer. Mechanistically, we found that increased C-C motif chemokine ligand 2 (CCL2) release mediated by TGM2 contributes to macrophage infiltration into the tumor microenvironment. CONCLUSIONS: Our results reveal the relevance and molecular networks of TG genes in human cancers and highlight the importance of TGM2 in pancreatic cancer, which may provide promising directions for immunotherapy and for addressing chemoresistance.

A comprehensive multiparameter flow cytometry panel for immune profiling and functional studies of frozen tissue, bone marrow, and spleen.

Flow cytometry (FC) is a highly informative technology that can provide valuable information about immune phenotype monitoring and immune cell states. However, there is a paucity of comprehensive panels developed and validated for use on frozen samples. Here, we developed a 17-plex flow cytometry panel to detect subtypes, frequencies, and functions of different immune cells that can be leveraged to study the different cellular characteristics in different disease models, physiological, and pathological conditions. This panel identifies surface markers to characterize T cells (CD8+, CD4+), natural killer (NK) cells and their subtypes (immature, cytotoxic, exhausted, activated),natural killer T (NKT) cells, neutrophils, macrophages (M1 (pro-inflammatory) and M2 (anti-inflammatory)), monocytes and their subtypes (classical and non-classical), dendritic cells (DC) and their subtypes (DC1, DC2), and eosinophils. The panel was designed to include only surface markers to avoid the necessity for fixation and permeabilization steps. This panel was optimized using cryopreserved cells. Immunophenotyping of spleen and bone marrow using the proposed panel was efficient in correctly differentiating the immune cell subtypes in inflammatory model of ligature-induced periodontitis, in which we found increased percentage of NKT cells, activated and mature/cytotoxic NK cells in the bone marrow of affected mice. This panel enables in-depth immunophenotyping of murine immune cells in bone marrow, spleen, tumors, and other non-immune tissues of mice. It could be a tool for systematic analysis of immune cell profiling in inflammatory conditions, systemic diseases, and tumor microenvironments.

TA-MSCs, TA-MSCs-EVs, MIF: their crosstalk in immunosuppressive tumor microenvironment.

As an important component of the immunosuppressive tumor microenvironment (TME), it has been established that mesenchymal stem cells (MSCs) promote the progression of tumor cells. MSCs can directly promote the proliferation, migration, and invasion of tumor cells via cytokines and chemokines, as well as promote tumor progression by regulating the functions of anti-tumor immune and immunosuppressive cells. MSCs-derived extracellular vesicles (MSCs-EVs) contain part of the plasma membrane and signaling factors from MSCs; therefore, they display similar effects on tumors in the immunosuppressive TME. The tumor-promoting role of macrophage migration inhibitory factor (MIF) in the immunosuppressive TME has also been revealed. Interestingly, MIF exerts similar effects to those of MSCs in the immunosuppressive TME. In this review, we summarized the main effects and related mechanisms of tumor-associated MSCs (TA-MSCs), TA-MSCs-EVs, and MIF on tumors, and described their relationships. On this basis, we hypothesized that TA-MSCs-EVs, the MIF axis, and TA-MSCs form a positive feedback loop with tumor cells, influencing the occurrence and development of tumors. The functions of these three factors in the TME may undergo dynamic changes with tumor growth and continuously affect tumor development. This provides a new idea for the targeted treatment of tumors with EVs carrying MIF inhibitors.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma.

Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586-0.671) in the TCGA cohort and 0.648 (95% CI: 0.535-0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC.

Multi-omics analysis of the expression and prognostic value of the butyrophilins in breast cancer.

Butyrophilins (BTNs) belong to the immunoglobulin superfamily of transmembrane proteins and play a role in the regulation of lymphocyte activation, several autoimmune diseases, and the progression of human cancers. However, the associated clinicopathologic characteristics and prognostic value of BTNs in breast cancer remain unknown. This study aimed to discover potential key related BTN genes and signaling pathways in breast cancer, which could provide new insights for immune-based strategies. In the present study, the mRNA expression level and prognostic value of BTN2A1, BTN3A1, BTN3A2, BTN3A3, BTNL2, BTNL9, ERMAP, and MOG were measured. Up-regulation of these genes was significantly correlated with improved overall and relapse-free survival. We then analyzed the prognostic outcomes of breast cancer subtypes, genetic alterations, interaction networks, and the functional enrichment of eight BTN family genes. Our results showed that these eight genes played essential roles in tumor progression. Furthermore, an immune infiltration analysis indicated that most candidate BTN family members were associated with intratumoral immune cell infiltration, especially that of γδ T cells. Finally, gene set enrichment analysis for a single hub gene revealed that each BTN gene played a vital role in tumor progression through immune signaling pathways. These findings provided new insights into breast cancer pathogenesis and identified eight potential biomarkers for breast cancer.

A mechanistic systems pharmacology modeling platform to investigate the effect of PD-L1 expression heterogeneity and dynamics on the efficacy of PD-1 and PD-L1 blocking antibodies in cancer.

Tumors have developed multitude of ways to evade immune response and suppress cytotoxic T cells. Programed cell death protein 1 (PD-1) and programed cell death ligand 1 (PD-L1) are immune checkpoints that when activated, rapidly inactivate the cytolytic activity of T cells. Expression heterogeneity of PD-L1 and the surface receptor dynamics of both PD-1 and PD-L1 may be important parameters in modulating the immune response. PD-L1 is expressed on both tumor and non-tumor immune cells and this differential expression reflects different aspects of anti-tumor immunity. Here, we developed a mechanistic computational model to investigate the role of PD-1 and PD-L1 dynamics in modulating the efficacy of PD-1 and PD-L1 blocking antibodies. Our model incorporates immunological synapse restricted interaction of PD-1 and PD-L1, basal parameters for receptor dynamics, and T cell interaction with tumor and non-tumor immune cells. Simulations predict the existence of a threshold in PD-1 expression above which there is no efficacy for both anti-PD-1 and anti-PD-L1. Model also predicts that anti-tumor response is more sensitive to PD-L1 expression on non-tumor immune cells than tumor cells. New combination strategies are suggested that may enhance efficacy in resistant cases such as combining anti-PD-1 with a low dose of anti-PD-L1 or with inhibitors of PD-L1 recycling and synthesis. Another combination strategy suggested by the model is the combination of anti-PD-1 and anti-PD-L1 with enhancers of PD-L1 degradation rate. Virtual patients are then generated to test specific biomarkers of response. Intriguing predictions that emerge from the virtual patient simulations are that PD-1 blocking antibody results in higher response rate than PD-L1 blockade and that PD-L1 expression density on non-tumor immune cells rather than tumor cells is a predictor of response.

Triple-negative breast cancer cells respond to T cells severely at the alternative splicing layer

BACKGROUND: Cross talk of tumor­immune cells at the gene expression level has been an area of intense research. However, it is largely unknown at the alternative splicing level which has been found to play important roles in the tumor­immune microenvironment. RESULTS: Here, we re-exploited one transcriptomic dataset to gain insight into tumor­immune interactions from the point of AS level. Our results showed that the AS profiles of triple-negative breast cancer cells co-cultured with activated T cells were significantly changed but not Estrogen receptor positive cells. We further suggested that the alteration in AS profiles in triple-negative breast cancer cells was largely caused by activated T cells rather than paracrine factors from activated T cells. Biological pathway analyses showed that translation initiation and tRNA aminoacylation pathways were most disturbed with T cell treatment. We also established an approach largely based on the AS factor­AS events associations and identified LSM7, an alternative splicing factor, may be responsible for the major altered events. CONCLUSIONS: Our study reveals the notable differences of response to T cells among breast cancer types which may facilitate the development or improvement of tumor immunotherapy.

CLDN10 associated with immune infiltration is a novel prognostic biomarker for clear cell renal cell carcinoma.

Aims: To identify the clinical roles of CLDN10 in clear cell renal cell carcinoma (ccRCC). Materials & methods: Using data from TCGA-KIRC, GEO DataSets and laboratory experiments to determine the prognostic and clinicopathological characteristics of CLDN10. Results: CLDN10 expression was remarkably reduced in ccRCC. Downregulated CLDN10 was related to metastasis and poor prognosis. Multivariate Cox analysis determined that elevated CLDN10 expression was independently correlated with longer OS and DFS. Moreover, CLDN10 expression was negatively associated with the methylation levels of cg10305311 and cg16275739. CLDN10 expression was also associated with naive CD4 and memory T-cell and dendritic cell infiltration. Conclusions: Immune-related CLDN10 is an independent prognostic biomarker of ccRCC. DNA hypermethylation plays an important role in decreased CLDN10 expression.

Tumor Microenvironment as a Regulator of Radiation Therapy: New Insights into Stromal-Mediated Radioresistance.

: A tumor is a complex "organ" composed of malignant cancer cells harboring genetic aberrations surrounded by a stroma comprised of non-malignant cells and an extracellular matrix. Considerable evidence has demonstrated that components of the genetically "normal" tumor stroma contribute to tumor progression and resistance to a wide array of treatment modalities, including radiotherapy. Cancer-associated fibroblasts can promote radioresistance through their secreted factors, contact-mediated signaling, downstream pro-survival signaling pathways, immunomodulatory effects, and cancer stem cell-generating role. The extracellular matrix can govern radiation responsiveness by influencing oxygen availability and controlling the stability and bioavailability of growth factors and cytokines. Immune status regarding the presence of pro- and anti-tumor immune cells can regulate how tumors respond to radiation therapy. Furthermore, stromal cells including endothelial cells and adipocytes can modulate radiosensitivity through their roles in angiogenesis and vasculogenesis, and their secreted adipokines, respectively. Thus, to successfully eradicate cancers, it is important to consider how tumor stroma components interact with and regulate the response to radiation. Detailed knowledge of these interactions will help build a preclinical rationale to support the use of stromal-targeting agents in combination with radiotherapy to increase radiosensitivity.