Immune Escape After Adoptive T-cell Therapy for Malignant Gliomas

Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy

BACKGROUND

Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized.

METHODS

We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database.

RESULTS

We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups.

CONCLUSION

Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

Importance of CD8 Tex cell-associated gene signatures in the prognosis and immunology of osteosarcoma

As a highly aggressive bone malignancy, osteosarcoma poses a significant therapeutic challenge, especially in the setting of metastasis or recurrence. This study aimed to investigate the potential of CD8-Tex cell-associated genes as prognostic biomarkers to reveal the immunogenomic profile of osteosarcoma and guide therapeutic decisions. mRNA expression data and clinical details of osteosarcoma patients were obtained from the TCGA database (TARGET-OS dataset). The GSE21257 dataset (from the GEO database) was used as an external validation set to provide additional information on osteosarcoma specimens. 84 samples from the TARGET-OS dataset were used as the training set, and 53 samples from the GSE21257 dataset served as the external validation cohort. Univariate Cox regression analysis was utilized to identify CD8 Tex cell genes associated with prognosis. The LASSO algorithm was performed for 1000 iterations to select the best subset to form the CD8 Tex cell gene signature (TRS). Final genes were identified using the multivariate Cox regression model of the LASSO algorithm. Risk scores were calculated to categorize patients into high- and low-risk groups, and clinical differences were explored by Kaplan-Meier survival analysis to assess model performance. Prediction maps were constructed to estimate 1-, 3-, and 5 year survival rates for osteosarcoma patients, including risk scores for CD8 Texcell gene markers and clinicopathologic factors. The ssGSEA algorithm was used to assess the differences in immune function between TRS-defined high- and low-risk groups. TME and immune cell infiltration were further assessed using the ESTIMATE and CIBERSORT algorithms. To explore the relationship between immune checkpoint gene expression levels and the two risk-defined groups. A CD8 Tex cell-associated gene signature was extracted from the TISCH database and prognostic markers including two genes were developed. The high-risk group showed lower survival, and model performance was validated by ROC curves and C-index. Predictive plots were constructed to demonstrate survival estimates, combining CD8 Tex cell gene markers and clinical factors. This study provides valuable insights into the molecular and immune characteristics of osteosarcoma and offers potential avenues for advances in therapeutic approaches.

Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

Comparing the efficacy of combined versus single immune cell adaptive therapy targeting colorectal cancer

PURPOSE

Colorectal cancer (CRC) is the most frequent cancer with limited therapeutic achievements. Recently, adoptive cellular immunotherapy has been developed as an antitumor therapy. However, its efficacy has not been tested in CRC. This study investigated the ability of an immune cell cocktail of dendritic cells (DCs), T cells, and natural killer (NK) cells to overcome immunological hurdles and improve the therapeutic efficacy of cell therapy for CRC.

METHODS

CRC lysate-pulsed monocyte-derived DCs (Mo-DCs), CRC antigen-specifically expanded T cells (CTL), and in vitro-expanded NK cells were cultured from patient peripheral blood mononuclear cells (PBMC). The ability of the combined immune cells to kill autologous tumor cells was investigated by co-culturing the combined immune cells with patient-derived tumor cells.

RESULTS

The Mo-DCs produced expressed T cell co-stimulating molecules like CD80, CD86, human leukocyte antigen (HLA)-DR and HLA-ABC, at high levels and were capable of activating naive T cells. The expanded T cells were predominantly CD8 T cells with high levels of CD8 effector memory cells and low levels of regulatory T cells. The NK cells expressed high levels of activating receptors and were capable of killing other cancer cell lines (K562 and HT29). The immune cell cocktail demonstrated a higher ability to kill autologous tumor cells than single types. An in vivo preclinical study confirmed the safety of the combined immune cell adaptive therapy showing no therapy-related death or general toxicity symptoms.

CONCLUSION

The results suggested that combined immune cell adaptive therapy could overcome the limited efficacy of cell immunotherapy.

Expanded specific T cells to hypomutated regions of the SARS-CoV-2 using mRNA electroporated antigen-presenting cells

The COVID-19 pandemic has caused about seven million deaths worldwide. Preventative vaccines have been developed including Spike gp mRNA-based vaccines that provide protection to immunocompetent patients. However, patients with primary immunodeficiencies, patients with cancer, or hematopoietic stem cell transplant recipients are not able to mount robust immune responses against current vaccine approaches. We propose to target structural SARS-CoV-2 antigens (i.e., Spike gp, Membrane, Nucleocapsid, and Envelope) using circulating human antigen-presenting cells electroporated with full length SARS-CoV-2 structural protein-encoding mRNAs to activate and expand specific T cells. Based on the Th1-type cytokine and cytolytic enzyme secretion upon antigen rechallenge, we were able to generate SARS-CoV-2 specific T cells in up to 70% of unexposed unvaccinated healthy donors (HDs) after 3 subsequent stimulations and in 100% of recovered patients (RPs) after 2 stimulations. By means of SARS-CoV-2 specific TCRβ repertoire analysis, T cells specific to Spike gp-derived hypomutated regions were identified in HDs and RPs despite viral genomic evolution. Hence, we demonstrated that SARS-CoV-2 mRNA-loaded antigen-presenting cells are effective activating and expanding COVID19-specific T cells. This approach represents an alternative to patients who are not able to mount adaptive immune responses to current COVID-19 vaccines with potential protection across new variants that have conserved genetic regions.

Macrophage-Related Gene Signatures for Predicting Prognosis and Immunotherapy of Lung Adenocarcinoma by Machine Learning and Bioinformatics

Background

In recent years, the immunotherapy of lung adenocarcinoma has developed rapidly, but the good therapeutic effect only exists in some patients, and most of the current predictors cannot predict it very well. Tumor-infiltrating macrophages have been reported to play a crucial role in lung adenocarcinoma (LUAD). Thus, we want to build novel molecular markers based on macrophages.

Methods

By non-negative matrix factorization (NMF) algorithm and Cox regression analysis, we constructed macrophage-related subtypes of LUAD patients and built a novel gene signature consisting of 12 differentially expressed genes between two subtypes. The gene signature was further validated in Gene-Expression Omnibus (GEO) datasets. Its predictive effect on prognosis and immunotherapy outcome was further evaluated with rounded analyses. We finally explore the role of TRIM28 in LUAD with a series of in vitro experiments.

Results

Our research indicated that a higher LMS score was significantly correlated with tumor staging, pathological grade, tumor node metastasis stage, and survival. LMS was identified as an independent risk factor for OS in LUAD patients and verified in GEO datasets. Clinical response to immunotherapy was better in patients with low LMS score compared to those with high LMS score. TRIM28, a key gene in the gene signature, was shown to promote the proliferation, invasion and migration of LUAD cell.

Conclusion

Our study highlights the significant role of gene signature in predicting the prognosis and immunotherapy efficacy of LUAD patients, and identifies TRIM28 as a potential biomarker for the treatment of LUAD.

mRNA-based precision targeting of neoantigens and tumor-associated antigens in malignant brain tumors

BACKGROUND

Despite advancements in the successful use of immunotherapy in treating a variety of solid tumors, applications in treating brain tumors have lagged considerably. This is due, at least in part, to the lack of well-characterized antigens expressed within brain tumors that can mediate tumor rejection; the low mutational burden of these tumors that limits the abundance of targetable neoantigens; and the immunologically "cold" tumor microenvironment that hampers the generation of sustained and productive immunologic responses. The field of mRNA-based therapeutics has experienced a boon following the universal approval of COVID-19 mRNA vaccines. mRNA-based immunotherapeutics have also garnered widespread interest for their potential to revolutionize cancer treatment. In this study, we developed a novel and scalable approach for the production of personalized mRNA-based therapeutics that target multiple tumor rejection antigens in a single therapy for the treatment of refractory brain tumors.

METHODS

Tumor-specific neoantigens and aberrantly overexpressed tumor-associated antigens were identified for glioblastoma and medulloblastoma tumors using our cancer immunogenomics pipeline called Open Reading Frame Antigen Network (O.R.A.N). Personalized tumor antigen-specific mRNA vaccine was developed for each individual tumor model using selective gene capture and enrichment strategy. The immunogenicity and efficacy of the personalized mRNA vaccines was evaluated in combination with anti-PD-1 immune checkpoint blockade therapy or adoptive cellular therapy with ex vivo expanded tumor antigen-specific lymphocytes in highly aggressive murine GBM models.

RESULTS

Our results demonstrate the effectiveness of the antigen-specific mRNA vaccines in eliciting robust anti-tumor immune responses in GBM hosts. Our findings substantiate an increase in tumor-infiltrating lymphocytes characterized by enhanced effector function, both intratumorally and systemically, after antigen-specific mRNA-directed immunotherapy, resulting in a favorable shift in the tumor microenvironment from immunologically cold to hot. Capacity to generate personalized mRNA vaccines targeting human GBM antigens was also demonstrated.

CONCLUSIONS

We have established a personalized and customizable mRNA-therapeutic approach that effectively targets a plurality of tumor antigens and demonstrated potent anti-tumor response in preclinical brain tumor models. This platform mRNA technology uniquely addresses the challenge of tumor heterogeneity and low antigen burden, two key deficiencies in targeting the classically immunotherapy-resistant CNS malignancies, and possibly other cold tumor types.

Bioconjugated liquid-like solid enhances characterization of solid tumor - chimeric antigen receptor T cell interactions

Chimeric antigen receptor (CAR) T cell therapy has demonstrated remarkable success as an immunotherapy for hematological malignancies, and its potential for treating solid tumors is an active area of research. However, limited trafficking and mobility of T cells within the tumor microenvironment (TME) present challenges for CAR T cell therapy in solid tumors. To gain a better understanding of CAR T cell function in solid tumors, we subjected CD70-specific CAR T cells to a challenge by evaluating their immune trafficking and infiltration through a confined 3D microchannel network in a bio-conjugated liquid-like solid (LLS) medium. Our results demonstrated successful CAR T cell migration and anti-tumor activity against CD70-expressing glioblastoma and osteosarcoma tumors. Through comprehensive analysis of cytokines and chemokines, combined with in situ imaging, we elucidated that immune recruitment occurred via chemotaxis, and the effector-to-target ratio plays an important role in overall antitumor function. Furthermore, through single-cell collection and transcriptomic profiling, we identified differential gene expression among the immune subpopulations. Our findings provide valuable insights into the complex dynamics of CAR T cell function in solid tumors, informing future research and development in this promising cancer treatment approach. STATEMENT OF SIGNIFICANCE: The use of specialized immune cells named CAR T cells to combat cancers has demonstrated remarkable success against blood cancers. However, this success is not replicated in solid tumors, such as brain or bone cancers, mainly due to the physical barriers of these solid tumors. Currently, preclinical technologies do not allow for reliable evaluation of tumor-immune cell interactions. To better study these specialized CAR T cells, we have developed an innovative in vitro three-dimensional model that promises to dissect the interactions between tumors and CAR T cells at the single-cell level. Our findings provide valuable insights into the complex dynamics of CAR T cell function in solid tumors, informing future research and development in this promising cancer treatment approach.

Machine Learning-Based Integration Develops a Macrophage-Related Index for Predicting Prognosis and Immunotherapy Response in Lung Adenocarcinoma

BACKGROUND

Macrophages play a critical role in tumor immune microenvironment (TIME) formation and cancer progression in lung adenocarcinoma (LUAD). However, few studies have comprehensively and systematically described the characteristics of macrophages in LUAD.

METHODS

This study identified macrophage-related markers with single-cell RNA sequencing data from the GSE189487 dataset. An integrative machine learning-based procedure based on 10 algorithms was developed to construct a macrophage-related index (MRI) in The Cancer Genome Atlas (TCGA), GSE30219, GSE31210, and GSE72094 datasets. Several algorithms were used to evaluate the associations of MRI with TIME and immunotherapy-related biomarkers. The role of MRI in predicting the immunotherapy response was evaluated with the GSE91061 dataset.

RESULTS

The optimal MRI constructed by the combination of the Lasso algorithm and plsRCox was an independent risk factor in LUAD and showed a stable and powerful performance in predicting the overall survival rate of patients with LUAD. Those with low MRI scores had a higher TIME score, a higher level of immune cells, a higher immunophenoscore, and a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a better response to immunotherapy. The IC50 value of common drugs for chemotherapy and target therapy with low MRI scores was higher compared to high MRI scores. Moreover, the survival prediction nomogram, developed from MRI, had good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of LUAD.

CONCLUSION

Our study constructed for the first time a consensus MRI for LUAD with 10 machine learning algorithms. The MRI could be helpful for risk stratification, prognosis, and selection of treatment approach in LUAD.

A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning

BACKGROUND

Ovarian cancer ranks the leading cause of gynecologic cancer-related death in the United States and the fifth most common cause of cancer-related mortality among American women. Increasing evidences have highlighted the vital role of macrophages M2/M1 proportion in tumor progression, prognosis and immunotherapy.

METHODS

Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers. Integrative procedure including 10 machine learning algorithms were performed to develop a prognostic macrophage related signature (MRS) with TCGA, GSE14764, GSE140082 datasets. The role of MRS in tumor microenvironment (TME) and therapy response was evaluated with the data of CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC, GSE91061 and IMvigor210 dataset.

RESULTS

The optimal MRS developed by the combination of CoxBoost and StepCox[forward] algorithm served as an independent risk factor in ovarian cancer. Compared with stage, grade and other established prognostic signatures, the current MRS had a better performance in predicting the overall survival rate of ovarian cancer patients. Low risk score indicated a higher TME score, higher level of immune cells, higher immunophenoscore, higher tumor mutational burden, lower TIDE score and lower IC50 value in ovarian cancer. The survival prediction nomogram had a good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of ovarian cancer patients.

CONCLUSION

All in all, the current study constructed a powerful prognostic MRS for ovarian cancer patients using 10 machine learning algorithms. This MRS could predict the prognosis and drug sensitivity in ovarian cancer.

Identification of M2-like macrophage-related signature for predicting the prognosis, ecosystem and immunotherapy response in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma is one of the most common malignancies worldwide, representing a big health-care challenge globally. M2-like macrophages are significantly correlated with tumor progression, metastasis and treatment resistance.

METHODS

Integrative 10 machine learning algorithms were performed to developed a M2-like macrophage related prognostic signature (MRPS). Single-cell RNA-sequencing analysis was performed to dissect the ecosystem of HCC. Several approaches, including TIDE score, immunophenoscore, TMB score and tumor escape score were used to evaluate the predictive role of MRPS in immunology response.

RESULTS

The optimal MRPS constructed by the combination of stepCox + superPC algorithm served as an independent risk factor and showed stable and powerful performances in predicting the overall survival rate of HCC patients with 2-, 3-, and 4-year AUCs of 0. 763, 0.751, and 0.699 in TCGA cohort. HCC patients with low risk score possessed a more interaction of immunoactivated cells, including NK, CD8+ cytotoxic T, and activated B, and a less interaction of immunosuppressive cells, including Treg, CD4+ exhauster T, and M2-like macrophage. Low risk score indicated a higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score and lower tumor escape score in HCC, suggesting a better immunotherapy response. The IC50 value of docetaxel, gemcitabine, crizotinib and Osimertinib in HCC with high risk score were lower versus that with low risk score. HCC patients with high risk score had a higher score of cancer-related hallmarks, including angiogenesis, DNA repair, EMT, glycolysis, and NOTCH signaling.

CONCLUSION

Our study proposed a novel MRPS for predicting the prognosis, ecosystem and immunotherapy response in HCC.

Prognostic and immunological significance of an M1 macrophage-related gene signature in osteosarcoma

As the most abundant infiltrating immune cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs) are pivotal in tumor development and treatment. The present investigation endeavors to explore the potential of M1 macrophage-related genes (MRGs) as biomarkers for assessing risk in individuals with osteosarcoma. RNA-sequence data and clinical data were derived from TCGA and GEO databases. The CIBERSORT method was utilized to discern subtypes of tumor-infiltrating immune cells. Identification of MRGs was achieved through Pearson correlation analysis. A prognostic risk model for MRGs was developed using Cox and LASSO regression analyses. A tripartite gene signature comprising CD37, GABRD, and ARHGAP25 was an independent prognostic indicator and was employed to develop a risk score model. The internal and external validation cohort confirmed the results. The area under the ROC curve (AUC) was determined for survival periods of 1 year, three years, and five years, yielding values of 0.746, 0.839, and 0.850, respectively. The C-index of the risk score was found to be superior to clinicopathological factors. GO/KEGG enrichment showed that the differences between high- and low-risk groups were predominantly associated with immune response pathways. Immune-related analysis related to proportions of immune cells, immune function, and expression levels of immune checkpoint genes all showed differences between the high- and low-risk groups. The qRT-PCR and Western blotting results indicate that CD37 expression was markedly higher in MG63 and U2OS cell lines when compared to normal osteoblast hFOB1.19. In U2OS cell line, GABRD expression levels were significantly upregulated. ARHGAP25 expression levels were elevated in both 143B and U2OS cell lines. In summary, utilizing a macrophage genes signature demonstrates efficacy in predicting both the prognosis and therapy response of OS. Additionally, immune analysis confirms a correlation between the risk score and the tumor microenvironment. Our findings, therefore, provide a cogent account for the disparate prognoses observed among patients and furnish a justification for further inquiry into biomarkers and anti-tumor treatment strategies.

Prognostic impact of MICALL1 and associates with immune infiltration in liver hepatocellular carcinoma patients

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is one of the most malignancy over the world. Previous studies have proven that Molecules Interacting with CasL-Like 1 (MICALL1) participated in cellular trafficking cascades, while there has no study to explore the function and carcinogenic mechanism MICALL1 in LIHC.

METHODS

We aimed to investigate the relationship between MICALL1 mRNA expression and LIHC using TCGA database. The expression of MICALL1 protein in clinic samples were examined by UALCAN database. Kaplan-Meier method was used for survival analysis. Logistic regression and Cox regression were performed to evaluate the prognostic significance of MICALL1. The MICALL1-binding protein were built by the STRING tool. Enrichment analysis by GO, KEGG and GSEA was used to explore possible function of MICALL1. The ssGSEA method was used to investigate the association between MICALL1 expression and the immune infiltration level in LIHC.

RESULTS

The expression and prognostic value of different MICAL family members in LIHC were evaluated. The expression of MICALL1 was significantly increased at both the transcript and protein levels in LIHC tissues. Further, the LIHC patients with high MICALL1 levels showed a worse OS, DSS and PFI. Some clinicopathologic features were identified to be related to MICALL1 expression in LIHC included clinical T stage, pathologic stage, histologic grade and AFP concentration. Univariate and multivariate survival analysis showed that MICALL1 was an independent prognostic marker for OS and DSS. Further enrichment analysis revealed that the K-RAS, TNFα/NF-κB and inflammatory response were significantly enriched in the high MICALL1 expression group. Immune infiltration analysis showed that high MICALL1 expression was correlated with infiltration level of macrophage cells, Th2 cells and some other immune cell types, including TFH.

CONCLUSIONS

MICALL1 expression was significantly associated with immune cell infiltration and may regarded as a promising prognostic biomarker for LIHC patients.

Three-Dimensional Bioconjugated Liquid-Like Solid (LLS) Enhance Characterization of Solid Tumor - Chimeric Antigen Receptor T cell interactions

Cancer immunotherapy offers lifesaving treatments for cancers, but the lack of reliable preclinical models that could enable the mechanistic studies of tumor-immune interactions hampers the identification of new therapeutic strategies. We hypothesized 3D confined microchannels, formed by interstitial space between bio-conjugated liquid-like solids (LLS), enable CAR T dynamic locomotion within an immunosuppressive TME to carry out anti-tumor function. Murine CD70-specific CAR T cells cocultured with the CD70-expressing glioblastoma and osteosarcoma demonstrated efficient trafficking, infiltration, and killing of cancer cells. The anti-tumor activity was clearly captured via longterm in situ imaging and supported by upregulation of cytokines and chemokines including IFNg, CXCL9, CXCL10, CCL2, CCL3, and CCL4. Interestingly, target cancer cells, upon an immune attack, initiated an "immune escape" response by frantically invading the surrounding microenvironment. This phenomenon however was not observed for the wild-type tumor samples which remained intact and produced no relevant cytokine response. Single cells collection and transcriptomic profiling of CAR T cells at regions of interest revealed feasibility of identifying differential gene expression amongst the immune subpopulations. Complimentary 3D in vitro platforms are necessary to uncover cancer immune biology mechanisms, as emphasized by the significant roles of the TME and its heterogeneity.

Immunotherapy reverses glioma-driven dysfunction of immune system homeostasis

BACKGROUND

Glioma-induced immune dysregulation of the hematopoietic system has been described in a limited number of studies. In this study, our group further demonstrates that gliomas interrupt the cellular differentiation programming and outcomes of hematopoietic stem and progenitor cells (HSPCs) in the bone marrow. HSPCs from glioma-bearing mice are reprogrammed and driven towards expansion of myeloid lineage precursors and myeloid-derived suppressor cells (MDSCs) in secondary lymphoid organs. However, we found this expansion is reversed by immunotherapy. Adoptive cellular therapy (ACT) has been demonstrably efficacious in multiple preclinical models of central nervous system (CNS) malignancies, and here we describe how glioma-induced dysfunction is reversed by this immunotherapeutic platform.

METHODS

The impact of orthotopic KR158B-luc glioma on HSPCs was evaluated in an unbiased fashion using single cell RNAseq (scRNAseq) of lineage- cells and phenotypically using flow cytometry. Mature myeloid cell frequencies and function were also evaluated using flow cytometry. Finally, ACT containing total body irradiation, tumor RNA-pulsed dendritic cells, tumor-reactive T cells and HSPCs isolated from glioma-bearing or non-tumor-bearing mice were used to evaluate cell fate differentiation and survival.

RESULTS

Using scRNAseq, we observed an altered HSPC landscape in glioma-bearing versus non-tumor-bearing mice . In addition, an expansion of myeloid lineage subsets, including granulocyte macrophage precursors (GMPs) and MDSCs, were observed in glioma-bearing mice relative to non-tumor-bearing controls. Furthermore, MDSCs from glioma-bearing mice demonstrated increased suppressive capacity toward tumor-specific T cells as compared with MDSCs from non-tumor-bearing hosts. Interestingly, treatment with ACT overcame these suppressive properties. When HSPCs from glioma-bearing mice were transferred in the context of ACT, we observed significant survival benefit and long-term cures in orthotopic glioma models compared with mice treated with ACT using non-glioma-bearing HSPCs.

Emerging frontiers in immuno- and gene therapy for cancer

BACKGROUND AIMS

The field of cell and gene therapy in oncology has moved rapidly since 2017 when the first cell and gene therapies, Kymriah followed by Yescarta, were approved by the Food and Drug Administration in the United States, followed by multiple other countries. Since those approvals, several new products have gone on to receive approval for additional indications. Meanwhile, efforts have been made to target different cancers, improve the logistics of delivery and reduce the cost associated with novel cell and gene therapies. Here, we highlight various cell and gene therapy-related technologies and advances that provide insight into how these new technologies will speed the translation of these therapies into the clinic.

CONCLUSIONS

In this review, we provide a broad overview of the current state of cell and gene therapy-based approaches for cancer treatment - discussing various effector cell types and their sources, recent advances in both CAR and non-CAR genetic modifications, and highlighting a few promising approaches for increasing in vivo efficacy and persistence of therapeutic drug products.

Comprehensive Molecular Analyses of a Macrophage-Related Gene Signature With Regard to Prognosis, Immune Features, and Biomarkers for Immunotherapy in Hepatocellular Carcinoma Based on WGCNA and the LASSO Algorithm

Macrophages have been reported to exert a crucial role in hepatocellular carcinoma (HCC). This study aimed to explore the macrophage-related genes and establish a macrophage-related signature (MRS) model to predict the overall survival (OS) of patients with HCC based on these genes’ expression. We screened the macrophage-related gene module by weighted gene coexpression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was utilized for further selection, and the selected genes were entered into stepwise regression to develop the MRS model, which was further validated in the Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) datasets. We analyzed the biological phenotypes associated with macrophages in terms of functional enrichment, tumor immune signature, and tumor mutational signature. The patient’s response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score, the immunophenotype score (IPS), and the IMvigor210 dataset. A novel MRS model was established based on the LASSO regression coefficients of the genes PON1, IL15RA, NEIL3, HILPDA, PFN2, HAVCR1, ANXA10, CDCA8, EPO, S100A9, TTK, KLRB1, SPP1, STC2, CYP26B1, GPC1, G6PD, and CBX2. In either dataset, MRS was identified as an independent risk factor for OS in HCC patients. Additionally, our research indicated that a high-risk score in the MRS model was significantly correlated with tumor staging, pathological grade, tumor–node–metastasis (TNM) stage, and survival. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were highly expressed in the high-risk group. In addition, the frequency of tumor mutations was also higher in the high-risk group. According to our analyses, a higher risk score in the MRS model may predict a better response to immunotherapy.

B cells and tumor immune escape

B lymphocyte is an important component of the human immune system and it has a role in the process of the body's specific immunity. In recent years, the research on B cells and tumor immune escape has rapidly progressed. Studies have shown that different types of B cells play different roles in tumor microenvironment through a variety of mechanisms. B cells in the tertiary lymphatic structure promote anti-tumor immunity, while regulatory B cells promote tumor immune escape. Antibody drugs targeting B cells are a promising direction for tumor immunotherapy.

Effects of immune checkpoint blockade on antigen-specific CD8+ T cells for use in adoptive cellular therapy

BACKGROUND

Adoptive T cell therapies have been successfully used as prophylaxis or treatment for immunocompromised patients at risk of viral infections or advanced cancers. Unfortunately, for some refractory cancers, they have failed. To overcome this, checkpoint inhibitors have shown to rescue immune anti-tumor responses. We hypothesized that in-vitro checkpoint blockade during T-cell stimulation and expansion with mRNA-pulsed dendritic cells may enhance the activity of antigen-specific T-cells and improve the efficacy of ACT platforms.

METHODS

Human PBMCs were isolated from CMV-seropositive donors to generate DCs. These were pulsed with CMVpp65-mRNA to educate T-cells in co-culture for 15-days. Three checkpoint blockade conditions were evaluated (anti-PD1, anti-Tim3 and anti-PD1+Tim3). IL-2 and antibodies blockades were added every 3 days. Immunophenotyping was performed on Day-0 and Day-15. Polyfunctional antigen-specific responses were evaluated upon rechallenge with CMVpp65 peptides.

RESULTS

CMVpp65 activated CD8+ T cells upregulate Lag3 and Tim3 (p= <0.0001). Tim3 antibody blockade alone or in combination led to a significant upregulation of Lag3 expression on CD8+pp65Tetramer+ central memory, effector memory, and TEMRA T-cells. This latter T-cell subset uniquely maintain double-positive Tim3/Lag3 expression after checkpoint blockade. In contrast, PD1 blockade had minimal effects on Tim3 or Lag3 expression. In addition, IFN-g secretion was reduced in T-cells treated with Tim3 blockade in a dose-dependent manner (p=0.004).

CONCLUSION

In this study, we have identified a potential activating component of Tim3 and linkage between Tim3 and Lag3 signaling upon blocking Tim3 axis during T cell/antigen presenting cell interactions that should be considered when targeting immune checkpoints for clinical use. This article is protected by copyright. All rights reserved.

Identification of hepatocellular carcinoma-related genes associated with macrophage differentiation based on bioinformatics analyses

Macrophage differentiation is associated with tumorigenesis, including the tumorigenesis of hepatocellular carcinoma (HCC). Herein, we explored the value of macrophage differentiation-associated genes (MDGs) in the prognosis of HCC using data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. We performed multivariate Cox regression analyses to identify the hub genes affecting HCC patient prognoses. The correlations between hub genes and macrophage differentiation and immune checkpoint inhibitors (PD-1, PD-L1, and CTLA4) were investigated. Finally, the potential mechanism was examined with gene set enrichment analysis (GSEA). In total, seventeen differentially expressed MDGs were obtained after intersecting data from the two databases. Multivariate analysis indicated that CDC42 expression was an independent prognostic indicator in both databases. Furthermore, CDC42 showed a strong correlation with the tumor infiltration levels of immune cells in HCC tissue. Correlation analysis revealed that CDC42 expression was positively associated with M2 macrophage markers and immune checkpoint inhibitors, which indicated that CDC42 expression might be related to M2 macrophage differentiation and HCC cell immune tolerance. Finally, GSEA showed that CDC42 expression was most significantly related to the Wnt signaling pathway. In conclusion, this study showed that CDC42 expression might be an important MDG in HCC and may prove to be a new gene for studying macrophage differentiation in HCC. Abbreviations: HCC: hepatocellular carcinoma; TCGA: The Cancer Genome Atlas; ICGC: International Cancer Genome Consortium; GSEA: gene set enrichment analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; ROC: receiver operating characteristic; K-M: Kaplan-Meier; AUC: the area under the ROC curve; TNM: Tumor size/lymph nodes/distance metastasis.

Is There a Role for Immunotherapy in Central Nervous System Cancers?

Glioblastoma has emerged as an immunotherapy-refractory tumor based on negative phase III studies of anti-programmed cell death-1 therapy among newly diagnosed as well as recurrent patients. In addition, although much work on vaccine and cellular approaches is ongoing, therapeutic benefit with these approaches has been underwhelming. Much scientific insight into the multitiered layers of immunosuppression exploited by glioblastoma tumors is emerging that sheds light on the explanation for the disappointing results to date and highlights possible therapeutic avenues that may offer a better likelihood of therapeutic benefit for immune-based therapies.

Ecology of Fear: Spines, Armor and Noxious Chemicals Deter Predators in Cancer and in Nature

In nature, many multicellular and unicellular organisms use constitutive defenses such as armor, spines, and noxious chemicals to keep predators at bay. These defenses render the prey difficult and/or dangerous to subdue and handle, which confers a strong deterrent for predators. The distinct benefit of this mode of defense is that prey can defend in place and continue activities such as foraging even under imminent threat of predation. The same qualitative types of armor-like, spine-like, and noxious defenses have evolved independently and repeatedly in nature, and we present evidence that cancer is no exception. Cancer cells exist in environments inundated with predator-like immune cells, so the ability of cancer cells to defend in place while foraging and proliferating would clearly be advantageous. We argue that these defenses repeatedly evolve in cancers and may be among the most advanced and important adaptations of cancers. By drawing parallels between several taxa exhibiting armor-like, spine-like, and noxious defenses, we present an overview of different ways these defenses can appear and emphasize how phenotypes that appear vastly different can nevertheless have the same essential functions. This cross-taxa comparison reveals how cancer phenotypes can be interpreted as anti-predator defenses, which can facilitate therapy approaches which aim to give the predators (the immune system) the upper hand. This cross-taxa comparison is also informative for evolutionary ecology. Cancer provides an opportunity to observe how prey evolve in the context of a unique predatory threat (the immune system) and varied environments.

Comprehensive Molecular Analyses of a Novel Mutational Signature Classification System with Regard to Prognosis, Genomic Alterations, and Immune Landscape in Glioma

Background: Glioma is the most common malignant brain tumor with complex carcinogenic process and poor prognosis. The current molecular classification cannot fully elucidate the molecular diversity of glioma.

Methods: Using broad public datasets, we performed cluster analysis based on the mutational signatures and further investigated the multidimensional heterogeneity of the novel glioma molecular subtypes. The clinical significance and immune landscape of four clusters also investigated. The nomogram was developed using the mutational clusters and clinical characteristics.

Results: Four heterogenous clusters were identified, termed C1, C2, C3, and C4, respectively. These clusters presented distinct molecular features: C1 was characterized by signature 1, PTEN mutation, chromosome seven amplification and chromosome 10 deletion; C2 was characterized by signature 8 and FLG mutation; C3 was characterized by signature 3 and 13, ATRX and TP53 mutations, and 11p15.1, 11p15.5, and 13q14.2 deletions; and C4 was characterized by signature 16, IDH1 mutation and chromosome 1p and 19q deletions. These clusters also varied in biological functions and immune status. We underlined the potential immune escape mechanisms: abundant stromal and immunosuppressive cells infiltration and immune checkpoints (ICPs) blockade in C1; lack of immune cells, low immunogenicity and antigen presentation defect in C2 and C4; and ICPs blockade in C3. Moreover, C4 possessed a better prognosis, and C1 and C3 were more likely to benefit from immunotherapy. A nomogram with excellent performance was also developed for assessing the prognosis of patients with glioma.

Conclusion: Our results can enhance the mastery of molecular features and facilitate the precise treatment and clinical management of glioma.

Impact of beta-2 microglobulin expression on the survival of glioma patients via modulating the tumor immune microenvironment

Aims

High immune cell infiltration in gliomas establishes an immunosuppressive tumor microenvironment, which in turn promotes resistance to immunotherapy. Hence, it is important to identify novel targets associated with high immune cell infiltration in gliomas. Our previous study showed that serum levels of beta‐2 microglobulin (B2M) in lower‐grade glioma patients were lower than those in glioblastoma patients. In the present study, we focused on exploring the roles of B2M in glioma immune infiltration.

Methods

A large cohort of patients with gliomas from the TCGA, CGGA, and Gravendeel databases was included to explore differential expression patterns and potential roles of B2M in gliomas. A total of 103 glioma tissue samples were collected to determine the distributions of B2M protein levels by immunofluorescent assays. Kaplan‐Meier survival analysis and meta‐analysis were used for survival analysis. GO(Gene‐ontology) enrichment analysis, co‐expression analysis, KEGG(Kyoto Encyclopedia of Genes and Genomes) pathway analysis, and immune infiltration analysis were performed to explore roles and related mechanisms of B2M in glioma.

Results

We found that both B2M mRNA and protein levels were abnormally upregulated in glioma samples compared with those from normal brain tissue. B2M expression was correlated with tumor grade and was downregulated in IDH1 mutant samples. Furthermore, B2M was a moderately sensitive indicator for predicting the mesenchymal molecular subtype of gliomas. Interestingly, glioma patients with lower B2M expression had remarkably longer survival times than those with higher B2M expression. Moreover, meta‐analysis showed that B2M was an independent predictive marker in glioma patients. The results of GO enrichment analysis revealed that B2M contributed to immune cell infiltration in glioma patients. In addition, results of KEGG pathway analysis and co‐expression analysis suggested that B2M may mediate glioma immune infiltration via chemokines.

Conclusions

We conclude that B2M levels are critical for the survival times of glioma patients, at least in part due to mediating high immune infiltration.

Promises and challenges of adoptive T-cell therapies for solid tumours

Cancer is a leading cause of death worldwide and, despite new targeted therapies and immunotherapies, many patients with advanced-stage- or high-risk cancers still die, owing to metastatic disease. Adoptive T-cell therapy, involving the autologous or allogeneic transplant of tumour-infiltrating lymphocytes or genetically modified T cells expressing novel T-cell receptors or chimeric antigen receptors, has shown promise in the treatment of cancer patients, leading to durable responses and, in some cases, cure. Technological advances in genomics, computational biology, immunology and cell manufacturing have brought the aspiration of individualised therapies for cancer patients closer to reality. This new era of cell-based individualised therapeutics challenges the traditional standards of therapeutic interventions and provides opportunities for a paradigm shift in our approach to cancer therapy. Invited speakers at a 2020 symposium discussed three areas-cancer genomics, cancer immunology and cell-therapy manufacturing-that are essential to the effective translation of T-cell therapies in the treatment of solid malignancies. Key advances have been made in understanding genetic intratumour heterogeneity, and strategies to accurately identify neoantigens, overcome T-cell exhaustion and circumvent tumour immunosuppression after cell-therapy infusion are being developed. Advances are being made in cell-manufacturing approaches that have the potential to establish cell-therapies as credible therapeutic options. T-cell therapies face many challenges but hold great promise for improving clinical outcomes for patients with solid tumours.

Tumor-treating fields (TTFields)-based cocktail therapy: a novel blueprint for glioblastoma treatment

Glioblastoma is one of the most common malignant tumors in the central nervous system. Due to the high plasticity, heterogeneity and complexity of the tumor microenvironment, these tumors are resistant to almost all therapeutic strategies when they reach an advanced stage. Along with being a unique and effective way to kill cancer cells, tumor-treating fields (TTFields) has emerged as a breakthrough among glioblastoma therapies since the advent of temozolomide (TMZ), and the combination of these treatments has gradually been promoted and applied in the clinic. The combination of TTFields with other therapies is particularly suitable for this type of "cold" tumors and has attracted a large amount of attention from clinicians and researchers in the era of cancer cocktail therapy. Here, we introduced the current treatment regimen for glioblastoma, highlighting the unique advantages of TTFields in the treatment of glioblastoma. Then, we summarized current glioblastoma clinical trials that combine TTFields and other therapies. In addition, the main and potential mechanisms of TTFields were introduced to further understand the rationale for each combination therapy. Finally, we focused on the most advanced technologies applied in glioblastoma research and treatment and the prospect of their combination with TTFields. This review provides a unique overview of glioblastoma treatment.

Regulation of MHC I Molecules in Glioblastoma Cells and the Sensitizing of NK Cells

Immunotherapy has been established as an important area in the therapy of malignant diseases. Immunogenicity sufficient for immune recognition and subsequent elimination can be bypassed by tumors through altered and/or reduced expression levels of major histocompatibility complex class I (MHC I) molecules. Natural killer (NK) cells can eliminate tumor cells in a MHC I antigen presentation-independent manner by an array of activating and inhibitory receptors, which are promising candidates for immunotherapy. Here we summarize the latest findings in recognizing and regulating MHC I molecules that affect NK cell surveillance of glioblastoma cells.