Dendritic Cell-Based Therapy Using LY6E Peptide with a Putative Role Against Colorectal Cancer

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer

BACKGROUND

Epigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC.

METHODS

A complete review of genomic data was performed to investigate the relationship between the epigenesis tumor microenvironment (TME) and machine learning algorithms in GC.

RESULTS

Firstly, TME-related differential expression of genes (DEGs) performed non-negative matrix factorization (NMF) clustering analysis and determined two clusters (C1 and C2). Then, Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) rates suggested that cluster C1 predicted a poorer prognosis. The Cox-LASSO regression analysis identified eight hub genes (SRMS, MET, OLFML2B, KIF24, CLDN9, RNF43, NETO2, and PRSS21) to build the TRG prognostic model and nine hub genes (TMPO, SLC25A15, SCRG1, ISL1, SOD3, GAD1, LOXL4, AKR1C2, and MAGEA3) to build the ERG prognostic model. Additionally, the signature's area under curve (AUC) values, survival rates, C-index scores, and mean squared error (RMS) curves were evaluated against those of previously published signatures, which revealed that the signature identified in this study performed comparably. Meanwhile, based on the IMvigor210 cohort, a statistically significant difference in OS between immunotherapy and risk scores was observed. It was followed by LASSO regression analysis which identified 17 key DEGs and a support vector machine (SVM) model identified 40 significant DEGs, and based on the Venn diagram, eight co-expression genes (ENPP6, VMP1, LY6E, SHISA6, TMEM158, SYT4, IL11, and KLK8) were discovered.

CONCLUSION

The study identified some hub genes that could be useful in predicting prognosis and management in GC.

Natural peptides for immunological regulation in cancer therapy: Mechanism, facts and perspectives

Cancer incidence and mortality rates are increasing annually. Treatment with surgery, chemotherapy and radiation therapy (RT) is unsatisfactory because many patients have advanced disease at the initial diagnosis. However, the emergence of immunotherapy promises to be an effective strategy to improve the outcome of advanced tumors. Immune checkpoint antibodies, which are at the forefront of immunotherapy, have had significant success but still leave some cancer patients without benefit. For more cancer patients to benefit from immunotherapy, it is necessary to find new drugs and combination therapeutic strategies to improve the outcome of advanced cancer patients and achieve long-term tumor control or even eradication. Peptides are promising choices for tumor immunotherapy drugs because they have the advantages of low production cost, high sequence selectivity, high tissue permeability, low toxicity and low immunogenicity etc., and the adjuvant matching and technologies like nanotechnology can further optimize the effects of peptides. In this review, we present the current status and mechanisms of research on peptides targeting multiple immune cells (T cells, natural killer (NK) cells, dendritic cells (DCs), tumor-associated macrophages (TAMs), regulatory T cells (Tregs)) and immune checkpoints in tumor immunotherapy; and we summarize the current status of research on peptide-based tumor immunotherapy in combination with other therapies including RT, chemotherapy, surgery, targeted therapy, cytokine therapy, adoptive cell therapy (ACT) and cancer vaccines. Finally, we discuss the current status of peptide applications in mRNA vaccine delivery.

Emerging Strategies of Engineering and Tracking Dendritic Cells for Cancer Immunotherapy

Dendritic cells (DCs), a kind of specialized immune cells, play key roles in antitumor immune response and promotion of innate and adaptive immune responses. Recently, many strategies have been developed to utilize DCs in cancer therapy, such as delivering antigens and adjuvants to DCs and using scaffold to recruit and activate DCs. Here we outline how different DC subsets influence antitumor immunity, summarize the FDA-approved vaccines and cancer vaccines under clinical trials, discuss the strategies for engineering DCs and noninvasive tracking of DCs to improve antitumor immunotherapy, and reveal the potential of artificial neural networks for the design of DC based vaccines.

Combination of pioglitazone and dendritic cell to optimize efficacy of immune cell therapy in CT26 tumor models

Introduction

The maturation faith of dendritic cells is restrained by the inflammatory environment and cytokines, such as interleukin-6 and its downstream component. Therefore, introducing the suitable antigen to dendritic cells is crucial. However, reducing the severity of the suppressive tumor microenvironment is indispensable. The present study examined the combination therapy of lymphocyte antigen 6 family member E (LY6E) pulsed mature dendritic cells (LPMDCs) and pioglitazone against colorectal cancer (CRC) to elevate the effectiveness of cancer treatment through probable role of pioglitazone on inhibiting IL-6/STAT3 pathway.

Methods

Dendritic cells were generated from murine bone marrow and were pulsed with lymphocyte antigen 6 family member E peptide to assess antigen-specific T-cell proliferation and cytotoxicity assay with Annexin/PI. The effect of pioglitazone on interleukin (IL)-6/STAT3 was evaluated in vitro by real-time polymerase chain reaction (PCR). Afterward, the CRC model was established by subcutaneous injection of CT26, mouse colon carcinoma cell line, in female mice. After treatment, tumor, spleen, and lymph nodes samples were removed for histopathological, ELISA, and real-time PCR analysis.

Results

In vitro results revealed the potential of lysate-pulsed dendritic cells in the proliferation of double-positive CD3-8 splenocytes and inducing immunogenic cell death responses, whereas pioglitazone declined the expression of IL-6/STAT3 in colorectal cell lines. In animal models, the recipient of LPMDCs combined with pioglitazone demonstrated high tumor-infiltrating lymphocytes. Elevating the IL-12 and interferon-gamma (IFN-γ) levels and prolonged survival in lysate-pulsed dendritic cell and combination groups were observed.

Conclusion

Pioglitazone could efficiently ameliorate the immunosuppressive feature of the tumor microenvironment, mainly through IL-6. Accordingly, applying this drug combined with LPMDCs provoked substantial CD8 positive responses in tumor-challenged animal models.

Multidimension Analysis of the Prognostic Value, Immune Regulatory Function, and ceRNA Network of LY6E in Individuals with Colorectal Cancer

BACKGROUND

Lymphocyte antigen 6 complex, locus E (LY6E) is abnormally expressed in several cancers and is associated with poor outcomes. However, the biological role of LY6E in colorectal cancer (CRC) remains largely unknown. Hence, we aimed to evaluate the expression levels, prognostic value, biological functions, and immune effects of LY6E via pan-cancer and CRC analyses using multiple databases.

METHODS

We analyzed the expression pattern of LY6E in various cancers. The prognostic value of LY6E expression was identified using the Kaplan-Meier analysis and the Cox regression models. We used gene set enrichment analysis (GSEA) to identify the potential functions of LY6E. Correlations between the LY6E expression and various factors, including LY6E methylation level, copy number variation (CNV), microsatellite instability (MSI), and immune checkpoint genes, were also analyzed. The levels of LY6E expression and immune infiltration were analyzed using CIBERSORT. We constructed a regulatory network that was in compliance with the competing endogenous RNA (ceRNA) hypothesis. A ceRNA expression-based nomogram was established. Real-time PCR (qRT-PCR) was applied to validate the expression of LY6E-related ceRNA in CRC cell lines.

RESULTS

LY6E is overexpressed in several tumor types, including CRC, and patients with high expression levels of LY6E have a poor prognosis. The Kaplan-Meier analysis and Cox regression analysis showed that LY6E could be considered a favorable prognostic factor in TCGA and GEO cohort. The results of GSEA showed that high LY6E expression levels were associated with immune-related pathways, such as those involved in antigen processing and presentation and the intestinal immune network for IgA production. Six methylation sites of LY6E that were associated with prognostic survival were screened. Moreover, the high levels of LY6E expression were correlated with copy number gain, microsatellite instability high, and immunotherapy response. The results of CIBERSORT analysis demonstrated that the LY6E expression levels were correlated with the infiltration of multiple immune cells, especially T cells. Then, we constructed a ceRNA network (LINC00963/miR-92a-3p/LY6E) and validated it using qRT-PCR. A predictive ceRNA-based nomogram was established and validated.

CONCLUSION

The oncogenic LY6E may serve as a promising marker for the diagnosis and treatment of CRC.

Understanding and improving cellular immunotherapies against cancer: From cell-manufacturing to tumor-immune models

The tumor microenvironment (TME) is shaped by dynamic metabolic and immune interactions between precancerous and cancerous tumor cells and stromal cells like epithelial cells, fibroblasts, endothelial cells, and hematopoietically-derived immune cells. The metabolic states of the TME, including the hypoxic and acidic niches, influence the immunosuppressive phenotypes of the stromal and immune cells, which confers resistance to both host-mediated tumor killing and therapeutics. Numerous in vitro TME platforms for studying immunotherapies, including cell therapies, are being developed. However, we do not yet understand which immune and stromal components are most critical and how much model complexity is needed to answer specific questions. In addition, scalable sourcing and quality-control of appropriate TME cells for reproducibly manufacturing these platforms remain challenging. In this regard, lessons from the manufacturing of immunomodulatory cell therapies could provide helpful guidance. Although immune cell therapies have shown unprecedented results in hematological cancers and hold promise in solid tumors, their manufacture poses significant scale, cost, and quality control challenges. This review first provides an overview of the in vivo TME, discussing the most influential cell populations in the tumor-immune landscape. Next, we summarize current approaches for cell therapies against cancers and the relevant manufacturing platforms. We then evaluate current immune-tumor models of the TME and immunotherapies, highlighting the complexity, architecture, function, and cell sources. Finally, we present the technical and fundamental knowledge gaps in both cell manufacturing systems and immune-TME models that must be addressed to elucidate the interactions between endogenous tumor immunity and exogenous engineered immunity.

Dendritic Cell-Based Immunotherapies and their Potential use in Colorectal Cancer Immunotherapy

Dendritic cells (DCs) are professional antigen-presenting cells, which are resident or proliferating in organs. Major histocompatibility complex (MHC) Class I and II on DCs in normal steady conditions process and present antigens including cancer antigens. Many approaches are used to enhance antigen presentation process of DCs and capture cancer cells. DCs are harvested from cancer patients and manipulated ex vivo in DC-based cancer immunotherapy. In addition, DCs' vaccines and other anticancer therapy combinations were discussed to optimize DCs' efficiency for cancer immunotherapy. This review addressed the use of the human conventional type-1 DCs, OX40⁺ plasmacytoid DCs, and DCs-derived exosomes. In addition, different combinations with DCs therapy such as combination with the monoclonal antibody, cytokine-induced killer cells, adjuvants, chemotherapy (DCs-based chemoimmunotherapy), and nanoparticles were listed and explored for their effectiveness against cancer, and mainly against colorectal cancer.