A Spatio-Temporal Model of Macrophage-Mediated Drug Resistance in Glioma Immunotherapy

A cuproptosis-based prognostic model for predicting survival in low-grade glioma

BACKGROUND

It is unknown what variables contribute to the formation and multiplication of low-grade gliomas (LGG). An emerging process of cell death is called cuproptosis. Our research aims to increase therapeutic options and gain a better understanding of the role that cuproptosis-related genes play in the physical characteristics of low-grade gliomas.

METHODS

The TCGA database was utilized to find cuproptosis genes that may be used to develop LGG risk model. Cox analysis in three different formats: univariate, multivariate, and LASSO. The gene signature's independent predictive ability was assessed using ROC curves and Cox regression analysis based on overall survival. Use of CGGA data and nomogram model for external validation Immunohistochemistry, gene mutation, and functional enrichment analysis are also employed to clarify risk models' involvement. Next, we analyzed changes in the immunological microenvironment in the risk model and forecasted possible chemotherapeutic drugs to target each group. Finally, we validated the protein expression levels of cuproptosis-related genes using LGG and adjacent normal tissues in a small self-case-control study.

RESULTS

This study developed a glioma predictive model based on five cuproptosis-associated genes. Compared to the high-risk group, the low-risk group's OS was significantly longer. The ROC curves showed high genetic signature performance in both groups. The signature-based categorisation was also linked to clinical characteristics and molecular subgroups. The prognosis of individuals with grade 2 or 3 glioma is also influenced by our risk model. Immunological testing revealed that the high-risk group had more immune cells and immunological function. The risk model also predicted immunotherapy and chemotherapy medication results. Also, this study confirmed that the expression of cuproptosis-related genes by Western blot.

CONCLUSION

We developed a prediction model for LGG patients using genes associated with cuproptosis. With acceptable prediction performance, this risk model may effectively stratify the prognosis of glioma patients.

Multiscale modeling of drug resistance in glioblastoma with gene mutations and angiogenesis

Drug resistance is a prominent impediment to the efficacy of targeted therapies across various cancer types, including glioblastoma (GBM). However, comprehending the intricate intracellular and extracellular mechanisms underlying drug resistance remains elusive. Empirical investigations have elucidated that genetic aberrations, such as gene mutations, along with microenvironmental adaptation, notably angiogenesis, act as pivotal drivers of tumor progression and drug resistance. Nonetheless, mathematical models frequently compartmentalize these factors in isolation. In this study, we present a multiscale agent-based model of GBM, encompassing cellular dynamics, intricate signaling pathways, gene mutations, angiogenesis, and therapeutic interventions. This integrative framework facilitates an exploration of the interplay between genetic mutations and the vascular microenvironment in shaping the dynamic evolution of tumors during treatment with tyrosine kinase inhibitor. Our simulations unveil that mutations influencing the migration and proliferation of tumor cells expedite the emergence of phenotype heterogeneity, thereby exacerbating tumor invasion under both treated and untreated conditions. Moreover, angiogenesis proximate to the tumor fosters a protumoral milieu, augmenting mutation-induced drug resistance by increasing the survival rate of tumor cells. Collectively, our findings underscore the dual roles of intrinsic genetic mutations and extrinsic microenvironmental adaptations in steering tumor growth and drug resistance. Finally, we substantiate our model predictions concerning the impact of gene mutations and angiogenesis on the responsiveness of targeted therapies by integrating single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, and clinical data from GBM patients. The multidimensional approach enhances our understanding of the complexities governing drug resistance in glioma and offers insights into potential therapeutic strategies.

miR‑137 is a diagnostic tumor‑suppressive miRNA that targets SPHK2 to promote M1‑type tumor‑associated macrophage polarization

The present study investigated the expression level of microRNA (miR)-137 in glioma tissues and cell lines and explored its potential diagnostic significance as well as its function effects on glioma cells. miR-137 expression level was detected in glioma tissues using in situ hybridization, and in glioma cell lines using reverse transcription-quantitative PCR (RT-qPCR). The diagnostic significance of miR-137 in glioma was assessed using receiver operating characteristic curve analyses. Quantibody® Human Inflammation Array 1 was used to evaluate the impact of ectopic miR-137 expression on release of cytokines in glioma cell lines. IL-13, TNF-α and IFN-γ levels were detected using ELISA. To confirm that sphingosine kinase 2 (SPHK2) is a target of miR-137, RT-qPCR, western blot analysis and dual-luciferase assay were adopted. The results demonstrated that miR-137 expression was downregulated in both glioma tissues and cell lines. Downregulation of miR-137 was significantly associated with high grade gliomas. Additionally, it was found that overexpression of miR-137 reduced IL-13, but promoted TNFα and IFN-γ production. SPHK2 knockdown inhibited IL-13 release, promoted TNF-α and IFN-γ production. SPHK2 was a direct target of miR-137. Collectively, the results of the present study indicated that miR-137 expression plays a tumor-suppressive role in glioma. It is downregulated in glioma and may promote M1-type TAMs polarization, and may be a diagnostic biomarker and potential therapeutic strategy for glioma treatment in the future.

Exploring Monocytes-Macrophages in Immune Microenvironment of Glioblastoma for the Design of Novel Therapeutic Strategies

Gliomas are primary malignant brain tumors. These tumors seem to be more and more frequent, not only because of a true increase in their incidence, but also due to the increase in life expectancy of the general population. Among gliomas, malignant gliomas and more specifically glioblastomas (GBM) are a challenge in their diagnosis and treatment. There are few effective therapies for these tumors, and patients with GBM fare poorly, even after aggressive surgery, chemotherapy, and radiation. Over the last decade, it is now appreciated that these tumors are composed of numerous distinct tumoral and non-tumoral cell populations, which could each influence the overall tumor biology and response to therapies. Monocytes have been proved to actively participate in tumor growth, giving rise to the support of tumor-associated macrophages (TAMs). In GBM, TAMs represent up to one half of the tumor mass cells, including both infiltrating macrophages and resident brain microglia. Infiltrating macrophages/monocytes constituted ~ 85% of the total TAM population, they have immune functions, and they can release a wide array of growth factors and cytokines in response to those factors produced by tumor and non-tumor cells from the tumor microenvironment (TME). A brief review of the literature shows that this cell population has been increasingly studied in GBM TME to understand its role in tumor progression and therapeutic resistance. Through the knowledge of its biology and protumoral function, the development of therapeutic strategies that employ their recruitment as well as the modulation of their immunological phenotype, and even the eradication of the cell population, can be harnessed for therapeutic benefit. This revision aims to summarize GBM TME and localization in tumor niches with special focus on TAM population, its origin and functions in tumor progression and resistance to conventional and experimental GBM treatments. Moreover, recent advances on the development of TAM cell targeting and new cellular therapeutic strategies based on monocyte/macrophages recruitment to eradicate GBM are discussed as complementary therapeutics.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Interaction of tumor-associated microglia/macrophages and cancer stem cells in glioma

Glioma is the most common tumor of the primary central nervous system, and its malignant phenotype has been shown to be closely related to glioma stem cells (GSCs). Although temozolomide has significantly improved the therapeutic outcome of glioma with a high penetration rate of the blood-brain barrier, resistance is often present in patients. Moreover, evidence has shown that the crosstalk between GSCs and tumor-associated microglia/macrophages (TAMs) affect the clinical occurrence, growth, and multi-tolerance of chemoradiotherapy in gliomas. Here, we highlight its vital roles in the maintenance of the stemness of GSCs and the ability of GSCs to recruit TAMs to the tumor microenvironment and promote their polarization into tumor-promoting macrophages, hence providing groundwork for future research into new treatment strategies of cancer.

Origin, activation, and targeted therapy of glioma-associated macrophages

The glioma tumor microenvironment plays a crucial role in the development, occurrence, and treatment of gliomas. Glioma-associated macrophages (GAMs) are the most widely infiltrated immune cells in the tumor microenvironment (TME) and one of the major cell populations that exert immune functions. GAMs typically originate from two cell types-brain-resident microglia (BRM) and bone marrow-derived monocytes (BMDM), depending on a variety of cytokines for recruitment and activation. GAMs mainly contain two functionally and morphologically distinct activation types- classically activated M1 macrophages (antitumor/immunostimulatory) and alternatively activated M2 macrophages (protumor/immunosuppressive). GAMs have been shown to affect multiple biological functions of gliomas, including promoting tumor growth and invasion, angiogenesis, energy metabolism, and treatment resistance. Both M1 and M2 macrophages are highly plastic and can polarize or interconvert under various malignant conditions. As the relationship between GAMs and gliomas has become more apparent, GAMs have long been one of the promising targets for glioma therapy, and many studies have demonstrated the therapeutic potential of this target. Here, we review the origin and activation of GAMs in gliomas, how they regulate tumor development and response to therapies, and current glioma therapeutic strategies targeting GAMs.

A Cuproptosis Activation Scoring model predicts neoplasm-immunity interactions and personalized treatments in glioma

Gliomas are malignant tumors in the central nervous system. Cuproptosis is a newly discovered cell death mechanism targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that cuproptosis participates in tumor progression, but its role in gliomas is still elusive. Here, we systematically explored the bulk-tumor and single-cell transcriptome data to reveal its role in gliomas. The cuproptosis activity score (CuAS) was constructed based on cuproptosis-related genes, and machine learning techniques validated the score stability. High CuAS gliomas were more likely to have a poor prognosis and an aggressive mesenchymal (MES) subtype. Subsequently, the SCENIC algorithm predicted 20 CuAS-related transcription factors (TFs) in gliomas. Function enrichment and microenvironment analyses found that CuAS was associated with tumor immune infiltration. Accordingly, intercellular communications between neoplasm and immunity were explored by the R package "Cellchat". Five signaling pathways and 8 ligand-receptor pairs including ICAM1, ITGAX, ITGB2, ANXA1-FRR1, and the like, were identified to suggest how cuproptosis activity connected neoplastic and immune cells. Critically, 13 potential drugs targeting high CuAs gliomas were predicted according to the CTRP and PRISM databases, including oligomycin A, dihydroartemisinin, and others. Taken together, cuproptosis is involved in glioma aggressiveness, neoplasm-immune interactions, and may be used to assist in drug selection.

Exosomal circRNA BTG2 derived from RBP-J overexpressed-macrophages inhibits glioma progression via miR-25-3p/PTEN

Macrophage-derived exosomes (Mφ-Exos) are involved in tumor progression, but its role in glioma is not fully understood. RBP-J is related to macrophage activation. In this study, we assess the role of exosomes derived from RBP-J-overexpressed macrophages (RBP-J OE Mφ-Exos) in glioma. The circular RNA (circRNA) profiles in RBP-J OE Mφ-Exos and THP-1-like macrophages (WT Mφ)-Exos were evaluated using circRNA microarray. Then the functions of Mφ-Exo-circRNA in glioma cells were assessed via CCK-8, EdU, Transwell invasion, and nude mouse assays. Besides, luciferase reporter assay, RNA immunoprecipitation, and Pearson's correlation analysis were adopted to confirm interactions. We found that circRNA BTG (circBTG2) is upregulated in RBP-J OE Mφ-Exos compared to WT Mφ-Exos. RBP-J OE Mφ-Exos co-culture and circBTG2 overexpression inhibited proliferation and invasion of glioma cells, whereas circBTG2 knockdown promotes tumor growth in vivo. The effects of RBP-J OE Mφ-Exos on glioma cells can be reversed by the circBTG2 knockdown. In conclusions, Exo-circBTG2 secreted from RBP-J OE Mφ inhibits tumor progression through the circBTG2/miR-25-3p/PTEN pathway, and circBTG2 is probably a diagnostic biomarker and potential target for glioma therapy.

The Chemokines Initiating and Maintaining Immune Hot Phenotype Are Prognostic in ICB of HNSCC

Background: The immune checkpoint blockade (ICB) with anti-programmed cell death protein 1(PD-1) on HNSCC is not as effective as on other tumors. In this study, we try to find out the key factors in the heterogeneous tumor-associated monocyte/macrophage (TAMM) that could regulate immune responses and predict the validity of ICB on HNSCC.

Experimental Design: To explore the correlation of the TAMM heterogeneity with the immune properties and prognosis of HNSCC, we established the differentiation trajectory of TAMM by analyzing the single-cell RNA-seq data of HNSCC, by which the HNSCC patients were divided into different sub-populations. Then, we exploited the topology of the network to screen out the genes critical for immune hot phenotype of HNSCC, as well as their roles in TAMM differentiation, tumor immune cycle, and progression. Finally, these key genes were used to construct a neural net model via deep-learning framework to predict the validity of treatment with anti-PD-1/PDL-1

Results: According to the differentiation trajectory, the genes involved in TAMM differentiation were categorized into early and later groups. Then, the early group genes divided the HNSCC patients into sub-populations with more detailed immune properties. Through network topology, CXCL9, 10, 11, and CLL5 related to TAMM differentiation in the TME were identified as the key genes initiating and maintaining the immune hot phenotype in HNSCC by remarkably strengthening immune responses and infiltration. Genome wide, CASP8 mutations were found to be key to triggering immune responses in the immune hot phenotype. On the other hand, in the immune cold phenotype, the evident changes in CNV resulted in immune evasion by disrupting immune balance. Finally, based on the framework of CXCL9-11, CLL5, CD8+, CD4+ T cells, and Macrophage M1, the neural network model could predict the validity of PD-1/PDL-1 therapy with 75% of AUC in the test cohort.

Conclusion: We concluded that the CXCL9, 10,11, and CCL5 mediated TAMM differentiation and constructed immune hot phenotype of HNSCC. Since they positively regulated immune cells and immune cycle in HNSCC, the CXCL9-11 and CCL5 could be used to predict the effects of anti-PD-1/PDL-1 therapy on HNSCC.

Tumor-associated macrophages related signature in glioma

Background: Glioma is the most common malignant primary tumor with a poor prognosis. Infiltration of tumor-associated macrophages (TAMs) is a hallmark of glioma. However, the regulatory mechanism of TAMs and the prognostic value of related signature in glioma remain unclear.

Methods: TAMs were analyzed by EPIC, MCPCOUNTER and XCELL methods in multiple cohorts, including the TCGA merged GBMLGG, CGGA mRNAseq-325, and CGGA mRNAseq-693. Weighted correlation network analysis (WGCNA) were performed to identify candidate hub genes that might be related to TAMs. The prognostic genes were selected by Univariate Cox regression, Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) multivariate Cox regression algorithm, and were used to construct a high efficacy prediction model.

Results: Compared with LGG, TAMs of GBM in the TCGA merged GBMLGG, CGGA mRNAseq-693, and CGGA mRNAseq-325 cohorts were increased, and high TAMs levels predicted poorer overall survival for gliomas. The prediction model constructed by nine prognostic genes was highly efficient. The TAMs related risk-score was an independent risk factor for glioma. Moreover, high risk score was correlated with an increased population of TAMs in glioma, as well as the high immune scores, stromal scores and ESTIMATE scores.

Conclusions: Increased TAMs might be an immune evasion mechanism of glioma. In addition, our findings suggested that TAMs-related signature was a valuable prognostic biomarker in glioma and provided therapeutic targets for glioma.

miR-9-5p Mediates ABCC1 to Elevate the Sensitivity of Glioma Cells to Temozolomide

Chemotherapy combined with surgery is an important clinical treatment for glioma, but endogenous or acquired temozolomide (TMZ) resistance can lead to poor prognosis. microRNA (miR)-9-5p acts in biological function of glioma, but the drug resistance of miR-9-5p in glioma is under exploration. The study intended to test the molecular mechanism of miR-9-5p in glioma cells. MTT assay was applied to investigate the chemosensitivity enhancement of miR-9-5p on TMZ in glioma cells U87-TMZ and U251-TMZ, and in vivo experiments confirmed its role on tumor growth in nude mice. The results of double luciferase reporter gene assay, qRT-PCR and WB indicated that miR-9-5p directly targeted ABCC1 (ATP binding cassette subfamily C member 1) to reduce its expressions. MTT and flow cytometry indicated that elevation of miR-9-5p or down-regulation of ABCC1 could inhibit proliferation-induced apoptosis of drug-resistant cells, and the decrease of miR-9-5p could reverse the reduction of ABCC1 on proliferation-induced apoptosis of drug-resistant cells. In vivo experiments showed that miR-9-5p could promote the anti-tumor role of TMZ. To sum up, the increase of miR-9-5p directly targets ABCC1 and may make glioma cells sensitive to TMZ.

The molecular feature of macrophages in tumor immune microenvironment of glioma patients

Background

Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth.

Methods

Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore.

Results

Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate.

Conclusion

Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.

Targeting CSF1R Alone or in Combination with PD1 in Experimental Glioma

Glioblastoma is an aggressive primary tumor of the central nervous system. Targeting the immunosuppressive glioblastoma-associated microenvironment is an interesting therapeutic approach. Tumor-associated macrophages represent an abundant population of tumor-infiltrating host cells with tumor-promoting features. The colony stimulating factor-1/ colony stimulating factor-1 receptor (CSF-1/CSF1R) axis plays an important role for macrophage differentiation and survival. We thus aimed at investigating the antiglioma activity of CSF1R inhibition alone or in combination with blockade of programmed death (PD) 1. We investigated combination treatments of anti-CSF1R alone or in combination with anti-PD1 antibodies in an orthotopic syngeneic glioma mouse model, evaluated post-treatment effects and assessed treatment-induced cytotoxicity in a coculture model of patient-derived microtumors (PDM) and autologous tumor-infiltrating lymphocytes (TILs) ex vivo. Anti-CSF1R monotherapy increased the latency until the onset of neurological symptoms. Combinations of anti-CSF1R and anti-PD1 antibodies led to longterm survivors in vivo. Furthermore, we observed treatment-induced cytotoxicity of combined anti-CSF1R and anti-PD1 treatment in the PDM/TILs cocultures ex vivo. Our results identify CSF1R as a promising therapeutic target for glioblastoma, potentially in combination with PD1 inhibition.

Recent Advances in Macrophage-Mediated Drug Delivery Systems

Macrophages have been extensively used in the development of drug delivery systems, as they can prolong the circulation and release of drugs, extend their half-life, increase their stability and targeting ability, and reduce immunogenicity. Moreover, they have good biocompatibility and degradability and offer abundant surface receptors for targeted delivery of a wide variety of drugs. Macrophage-mediated drug delivery systems can be prepared by loading drugs or drug-loaded nanoparticles into macrophages, macrophage membranes or macrophage-derived vesicles. Although such systems can be used to treat inflammation, cancer, HIV infection and other diseases, they require further research and optimization since they have been assembled from diverse sources and therefore can have quite different physical and chemical properties. Moreover, potential cell-drug interactions can limit their application, and the biological activity of membrane proteins might be lost during membrane extraction and storage. In this review, we summarize the recent advances in this field and discuss the preparation of macrophage-mediated drug delivery systems, their advantages over other delivery systems, their potential applications and future lines of research.

The role of MiRNA-21 in gliomas: Hope for a novel therapeutic intervention?

Gliomas are the most common primary brain tumors in adults. They are generally very resistant to treatment and are therefore associated with negative outcomes. MicroRNAs (miRNAs) are small, non-coding RNA molecules that affect many cellular processes by regulating gene expression and, post-transcriptionally, the translation of mRNAs. MiRNA-21 has been consistently shown to be upregulated in glioma and research has shown that it is involved in a wide variety of biological pathways, promoting tumor cell survival and invasiveness. Furthermore, it has been implicated in resistance to treatment, both against chemotherapy and radiotherapy. In this review, we gathered the existent data on miRNA-21 and gliomas, in terms of its expression levels, association with grade and prognosis, the pathways it involves and its targets in glioma, and finally how it leads to treatment resistance. Furthermore, we discuss how this knowledge could be applied in clinical practice in the years to come. To our knowledge, this is the first review to assess in extent and depth the role of miRNA-21 in gliomas.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Modeling immune cell behavior across scales in cancer

Detailed, mechanistic models of immune cell behavior across multiple scales in the context of cancer provide clinically relevant insights needed to understand existing immunotherapies and develop more optimal treatment strategies. We highlight mechanistic models of immune cells and their ability to become activated and promote tumor cell killing. These models capture various aspects of immune cells: (a) single‐cell behavior by predicting the dynamics of intracellular signaling networks in individual immune cells, (b) multicellular interactions between tumor and immune cells, and (c) multiscale dynamics across space and different levels of biological organization. Computational modeling is shown to provide detailed quantitative insight into immune cell behavior and immunotherapeutic strategies. However, there are gaps in the literature, and we suggest areas where additional modeling efforts should be focused to more prominently impact our understanding of the complexities of the immune system in the context of cancer.

This article is categorized under:

Biological Mechanisms > Cell Signaling

Models of Systems Properties and Processes > Mechanistic Models

Models of Systems Properties and Processes > Cellular Models

Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq

Tumor-infiltrating myeloid cells are the most abundant leukocyte population within tumors. Molecular cues from the tumor microenvironment promote the differentiation of immature myeloid cells toward an immunosuppressive phenotype. However, the in situ dynamics of the transcriptional reprogramming underlying this process are poorly understood. Therefore, we applied single cell RNA-seq (scRNA-seq) to computationally investigate the cellular composition and transcriptional dynamics of tumor and adjacent normal tissues from 4 early-stage non-small cell lung cancer (NSCLC) patients. Our scRNA-seq analyses identified 11 485 cells that varied in identity and gene expression traits between normal and tumor tissues. Among these, myeloid cell populations exhibited the most diverse changes between tumor and normal tissues, consistent with tumor-mediated reprogramming. Through trajectory analysis, we identified a differentiation path from CD14+ monocytes to M2 macrophages (monocyte-to-M2). This differentiation path was reproducible across patients, accompanied by increased expression of genes (eg, MRC1/CD206, MSR1/CD204, PPARG, TREM2) with significantly enriched functions (Oxidative phosphorylation and P53 pathway) and decreased expression of genes (eg, CXCL2, IL1B) with significantly enriched functions (TNF-α signaling via NF-κB and inflammatory response). Our analysis further identified a co-regulatory network implicating upstream transcription factors (JUN, NFKBIA) in monocyte-to-M2 differentiation, and activated ligand-receptor interactions (eg, SFTPA1-TLR2, ICAM1-ITGAM) suggesting intratumoral mechanisms whereby epithelial cells stimulate monocyte-to-M2 differentiation. Overall, our study identified the prevalent monocyte-to-M2 differentiation in NSCLC, accompanied by an intricate transcriptional reprogramming mediated by specific transcriptional activators and intercellular crosstalk involving ligand-receptor interactions.

Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas

BACKGROUND

The tumor-associated microenvironment plays important roles in tumor progression and drug resistance. However, systematic investigations of macrophage-tumor cell interactions to identify novel macrophage-related gene signatures in gliomas for predicting patient prognoses and responses to targeted therapies are lacking.

METHODS

We developed a multicellular gene network approach to investigating the prognostic role of macrophage-tumor cell interactions in tumor progression and drug resistance in gliomas. Multicellular gene networks connecting macrophages and tumor cells were constructed from re-grouped drug-sensitive and drug-resistant samples of RNA-seq data in mice gliomas treated with BLZ945 (a CSF1R inhibitor). Subsequently, a differential network-based COX regression model was built to identify the risk signature using a cohort of 310 glioma samples from the Chinese Glioma Genome Atlas database. A large independent validation set of 690 glioma samples from The Cancer Genome Atlas database was used to test the prognostic significance and accuracy of the gene signature in predicting prognosis and targeted therapeutic response of glioma patients.

RESULTS

A macrophage-related gene signature was developed consisting of twelve genes (ANPEP, DPP4, PRRG1, GPNMB, TMEM26, PXDN, CDH6, SCN3A, SEMA6B, CCDC37, FANCA, NETO2), which was tested in the independent validation set to examine its prognostic significance and accuracy. The generation of 1000 random gene signatures by a bootstrapping scheme justified the non-random nature of the macrophage-related gene signature. Moreover, the discovered gene signature was verified to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics and outperformed other existing gene signatures. Additionally, the macrophage-related gene signature was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures.

CONCLUSION

The multicellular gene network approach developed herein indicates profound roles of the macrophage-mediated tumor microenvironment in the progression and drug resistance of gliomas. The identified macrophage-related gene signature has good prognostic value for predicting resistance to targeted therapeutics and survival of glioma patients, implying that combining current targeted therapies with new macrophage-targeted therapy may be beneficial for the long-term treatment outcomes of glioma patients.

Multiscale modeling reveals angiogenesis-induced drug resistance in brain tumors and predicts a synergistic drug combination targeting EGFR and VEGFR pathways

BACKGROUND

Experimental studies have demonstrated that both the extracellular vasculature or microenvironment and intracellular molecular network (e.g., epidermal growth factor receptor (EGFR) signaling pathway) are important for brain tumor growth. Additionally, some drugs have been developed to inhibit EGFR signaling pathways. However, how angiogenesis affects the response of tumor cells to drug treatment has rarely been mechanistically studied. Therefore, a multiscale model is required to investigate such complex biological systems that contain interactions and feedback among multiple levels.

RESULTS

In this study, we developed a single cell-based multiscale spatiotemporal model to simulate vascular tumor growth and the drug response based on the vascular endothelial growth factor receptor (VEGFR) signaling pathway, the EGFR signaling pathway and the cell cycle as well as several microenvironmental factors that determine cell fate switches in a temporal and spatial context. By incorporating the EGFRI treatment effect, the model showed an interesting phenomenon in which the survival rate of tumor cells decreased in the early stage but rebounded in a later stage, revealing the emergence of drug resistance. Moreover, we revealed the critical role of angiogenesis in acquired drug resistance, since inhibiting blood vessel growth using a VEGFR inhibitor prevented the recovery of the survival rate of tumor cells in the later stage. We further investigated the optimal timing of combining VEGFR inhibition with EGFR inhibition and predicted that the drug combination targeting both the EGFR pathway and VEGFR pathway has a synergistic effect. The experimental data validated the prediction of drug synergy, confirming the effectiveness of our model. In addition, the combination of EGFR and VEGFR genes showed clinical relevance in glioma patients.

CONCLUSIONS

The developed multiscale model revealed angiogenesis-induced drug resistance mechanisms of brain tumors to EGFRI treatment and predicted a synergistic drug combination targeting both EGFR and VEGFR pathways with optimal combination timing. This study explored the mechanistic and functional mechanisms of the angiogenesis underlying tumor growth and drug resistance, which advances our understanding of novel mechanisms of drug resistance and provides implications for designing more effective cancer therapies.

Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas

Occurrence and development of cancers are governed by complex networks of interacting intercellular and intracellular signals. The technology of single-cell RNA sequencing (scRNA-seq) provides an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated microenvironment. Here we combined scRNA-seq data with clinical bulk gene expression data to develop a computational pipeline for identifying the prognostic and predictive signature that connects cancer cells and microenvironmental cells. The pipeline was applied to glioma scRNA-seq data and revealed a tumor-associated microglia/macrophage-mediated EGFR/ERBB2 feedback-crosstalk signaling module, which was defined as a multilayer network biomarker (MNB) to predict survival outcome and therapeutic response of glioma patients. We used publicly available clinical data sets from large cohorts of glioma patients to examine the prognostic significance and predictive accuracy of the MNB, which outperformed conventional gene biomarkers and other methods. Additionally, the MNB was found to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics. Moreover, the MNB was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. The robustness of the MNB was further tested on additional data sets. Our study presents a promising scRNA-seq transcriptome-based multilayer network approach to elucidate the interactions between tumor cell and tumor-associated microenvironment and to identify prognostic and predictive signatures of cancer patients. The proposed MNB method may facilitate the design of more effective biomarkers for predicting prognosis and therapeutic resistance of cancer patients.

Glioma stem cells reconstruct similar immunoinflammatory microenvironment in different transplant sites and induce malignant transformation of tumor microenvironment cells

PURPOSE

This study aimed to examine whether the different tumor-transplanted sites could construct a similar immunoinflammatory microenvironment and to investigate the interactions between tumor microenvironment cells.

METHODS

The red fluorescent protein-SU3 (SU3-RFP) or SU3 glioma stem cells (GSC) were inoculated into the brain, liver, abdominal cavity, and subcutis of green fluorescent protein (GFP)-nude mice. The tumor tissues were taken to observe the tissue cell distribution. The single cell suspension of tumor tissues was prepared and cultured, while the SU3-RFP cells were co-cultured with the cells from GFP-transgenic mice. The RFP+, GFP+, and RFP+/GFP+ cells were traced by fluorescence microscope, and their protein expressions were determined by Western blot analysis. The markers of immunoinflammatory cells, including F4/80, CD11b, CD11c, CD80, CD47, and SIRP-α, were determined by RT-PCR and immunocytochemistry assays, respectively.

RESULTS

The xenograft models of all transplant sites were inducible, and the red tumor cells of tumor tissues were encircled by a great quantity of host-derived green cells, including immunoinflammatory cells with CD80, F4/80, CD11b, and CD11c expressions, which might generate the cell colonies and possess the pseudopodia. Additionally, the interactions between red tumor cells and green immunoinflammatory cells, including cell fusion process and yellow fusion cell formation, were observed in cultured cells. The fusion cells-derived B4 cells with expressions of CD47 and SIRP-α proteins had the strong proliferation ability and tumorigenic effect.

CONCLUSIONS

The similar tumor immunoinflammatory microenvironment was constructed by GSC in different transplant sites, and the cell fusion indicated a malignant transformation of the tumor microenvironment cells.