Single-cell spatial immune landscapes of primary and metastatic brain tumours

Myeloid Cells Pave the Metastatic Road in Breast Cancer

Current immunotherapeutic approaches are tailored towards biomarkers and mechanisms presented in the primary tumor microenvironment, yet few are designed against targets that arise in the metastatic site. In a recent Cancer Discovery article, Yofe and colleagues spatially and temporally mapped the evolving breast tumor metastatic environment within the lung at single-cell resolution to identify immunologic cell types and mechanisms underlying the formation of metastatic nodules. The authors identified myeloid cell, monocyte, and neutrophil phenotypes as the most significant differences between the primary tumor and metastatic site. The authors also revealed the existence of a metastasis-specific population of macrophages expressing Trem2 in both human and murine models that exhibit immunosuppressive features. Interestingly, Trem2+ macrophages are enriched at the invasive border of metastatic nodules compared with the metastatic core, suggesting they play an exclusionary role within the site. Characterization of the premetastatic lung in conjunction with pseudotime lineage analysis revealed that Fn1+ monocytes precede metastasis formation and are the likely source of Trem2+ macrophages in developed nodules. This study supports the characterization of metastatic immune microenvironments to identify novel immunotherapeutic targets that may not exist within the primary site but play a significant role in patient outcome.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity

Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.

Harnessing the innate immune system by revolutionizing macrophage-mediated cancer immunotherapy

Immunotherapy is a promising and safer alternative to conventional cancer therapies. It involves adaptive T-cell therapy, cancer vaccines, monoclonal antibodies, immune checkpoint blockade (ICB), and chimeric antigen receptor (CAR) based therapies. However, most of these modalities encounter restrictions in solid tumours owing to a dense, highly hypoxic and immune-suppressive microenvironment as well as the heterogeneity of tumour antigens. The elevated intra-tumoural pressure and mutational rates within fastgrowing solid tumours present challenges in efficient drug targeting and delivery. The tumour microenvironment is a dynamic niche infiltrated by a variety of immune cells, most of which are macrophages. Since they form a part of the innate immune system, targeting macrophages has become a plausible immunotherapeutic approach. In this review, we discuss several versatile approaches (both at pre-clinical and clinical stages) such as the direct killing of tumour-associated macrophages, reprogramming pro-tumour macrophages to anti-tumour phenotypes, inhibition of macrophage recruitment into the tumour microenvironment, novel CAR macrophages, and genetically engineered macrophages that have been devised thus far. These strategies comprise a strong and adaptable macrophage-toolkit in the ongoing fight against cancer and by understanding their significance, we may unlock the full potential of these immune cells in cancer therapy.

The Anti-hypoxia Potentials of Trans-sodium Crocetinate in Hypoxiarelated Diseases: A Review

Crocetin is a kind of apocarotenoid carboxylic acid extracted from saffron (Crocus sativus L.), which is effective in upregulating tissue oxygenation. However, crocetin is difficult to solubilize. It was shown that the trans isomer of crocetin is effective in improving oxygen diffusivity, while its cis isomer appears not to be. Hence, the isolated trans isomer of crocetin or trans-sodium crocetinate (TSC) can be used instead of crocetin. It is shown that TSC can upregulate hypoxic tissue oxygenation and be effective in treating some hypoxia-related diseases. Moreover, experimental and clinical studies have reported no adverse effects following TSC treatment, even at high doses. The current study will discuss the potential role of TSC in hemorrhagic shock, ischemia, brain tumor radiotherapy, and others.

Enrichment of a neutrophil-like monocyte transcriptional state in glioblastoma myeloid suppressor cells

Glioblastomas (GBM) are lethal central nervous system cancers associated with tumor and systemic immunosuppression. Heterogeneous monocyte myeloid-derived suppressor cells (M-MDSC) are implicated in the altered immune response in GBM, but M-MDSC ontogeny and definitive phenotypic markers are unknown. Using single-cell transcriptomics, we revealed heterogeneity in blood M-MDSC from GBM subjects and an enrichment in a transcriptional state reminiscent of neutrophil-like monocytes (NeuMo), a newly described pathway of monopoiesis in mice. Human NeuMo gene expression and Neu-like deconvolution fraction algorithms were created to quantitate the enrichment of this transcriptional state in GBM subjects. NeuMo populations were also observed in M-MDSCs from lung and head and neck cancer subjects. Dexamethasone (DEX) and prednisone exposures increased the usage of Neu-like states, which were inversely associated with tumor purity and survival in isocitrate dehydrogenase wildtype (IDH WT) gliomas. Anti-inflammatory ZC3HA12/Regnase-1 transcripts were highly correlated with NeuMo expression in tumors and in blood M-MDSC from GBM, lung, and head and neck cancer subjects. Additional novel transcripts of immune-modulating proteins were identified. Collectively, these findings provide a framework for understanding the heterogeneity of M-MDSCs in GBM as cells with different clonal histories and may reshape approaches to study and therapeutically target these cells.

Pembrolizumab alters the tumor immune landscape in a patient with dMMR glioblastoma

Congenital DNA mismatch repair defects (dMMR), such as Lynch Syndrome, predispose patients to a variety of cancers and account for approximately 1% of glioblastoma cases. While few therapeutic options exist for glioblastoma, checkpoint blockade therapy has proven effective in dMMR tumors. Here we present a case study of a male in their 30s diagnosed with dMMR glioblastoma treated with pembrolizumab who experienced a partial response to therapy. Using a multiplex IHC analysis pipeline on archived slide specimens from tumor resections at diagnosis and after therapeutic interventions, we quantified changes in the frequency and spatial distribution of key cell populations in the tumor tissue. Notably, proliferating (KI67+) macrophages and T cells increased in frequency as did other KI67+ cells within the tumor. Therapeutic intervention remodeled the cellular spatial distribution in the tumor leading to a greater frequency of macrophage/tumor cell interactions and T cell/T cell interactions, highlighting impacts of checkpoint blockade on tumor cytoarchitecture and revealing spatial patterns that may indicate advantageous immune interactions in glioma and other solid tumors treated with these agents.

Harnessing immunotherapy for brain metastases: insights into tumor-brain microenvironment interactions and emerging treatment modalities

Brain metastases signify a deleterious milestone in the progression of several advanced cancers, predominantly originating from lung, breast and melanoma malignancies, with a median survival timeframe nearing six months. Existing therapeutic regimens yield suboptimal outcomes; however, burgeoning insights into the tumor microenvironment, particularly the immunosuppressive milieu engendered by tumor-brain interplay, posit immunotherapy as a promising avenue for ameliorating brain metastases. In this review, we meticulously delineate the research advancements concerning the microenvironment of brain metastases, striving to elucidate the panorama of their onset and evolution. We encapsulate three emergent immunotherapeutic strategies, namely immune checkpoint inhibition, chimeric antigen receptor (CAR) T cell transplantation and glial cell-targeted immunoenhancement. We underscore the imperative of aligning immunotherapy development with in-depth understanding of the tumor microenvironment and engendering innovative delivery platforms. Moreover, the integration with established or avant-garde physical methodologies and localized applications warrants consideration in the prevailing therapeutic schema.

Glioblastoma-instructed microglia transition to heterogeneous phenotypic states with phagocytic and dendritic cell-like features in patient tumors and patient-derived orthotopic xenografts

Background

A major contributing factor to glioblastoma (GBM) development and progression is its ability to evade the immune system by creating an immune-suppressive environment, where GBM-associated myeloid cells, including resident microglia and peripheral monocyte-derived macrophages, play critical pro-tumoral roles. However, it is unclear whether recruited myeloid cells are phenotypically and functionally identical in GBM patients and whether this heterogeneity is recapitulated in patient-derived orthotopic xenografts (PDOXs). A thorough understanding of the GBM ecosystem and its recapitulation in preclinical models is currently missing, leading to inaccurate results and failures of clinical trials.

Methods

Here, we report systematic characterization of the tumor microenvironment (TME) in GBM PDOXs and patient tumors at the single-cell and spatial levels. We applied single-cell RNA-sequencing, spatial transcriptomics, multicolor flow cytometry, immunohistochemistry and functional studies to examine the heterogeneous TME instructed by GBM cells. GBM PDOXs representing different tumor phenotypes were compared to glioma mouse GL261 syngeneic model and patient tumors.

Results

We show that GBM tumor cells reciprocally interact with host cells to create a GBM patient-specific TME in PDOXs. We detected the most prominent transcriptomic adaptations in myeloid cells, with brain-resident microglia representing the main population in the cellular tumor, while peripheral-derived myeloid cells infiltrated the brain at sites of blood-brain barrier disruption. More specifically, we show that GBM-educated microglia undergo transition to diverse phenotypic states across distinct GBM landscapes and tumor niches. GBM-educated microglia subsets display phagocytic and dendritic cell-like gene expression programs. Additionally, we found novel microglial states expressing cell cycle programs, astrocytic or endothelial markers. Lastly, we show that temozolomide treatment leads to transcriptomic plasticity and altered crosstalk between GBM tumor cells and adjacent TME components.

Conclusions

Our data provide novel insights into the phenotypic adaptation of the heterogeneous TME instructed by GBM tumors. We show the key role of microglial phenotypic states in supporting GBM tumor growth and response to treatment. Our data place PDOXs as relevant models to assess the functionality of the TME and changes in the GBM ecosystem upon treatment.

Identification of RP11-770J1.4 as immune-related lncRNA regulating the CTXN1-cGAS-STING axis in histologically lower-grade glioma

Human gliomas are lethal brain cancers. Emerging evidence revealed the regulatory role of long noncoding RNAs (lncRNAs) in tumors. Here, we performed a comprehensive analysis of the expression profiles of RNAs in histologically lower-grade glioma (LGG). Enrichment analysis revealed that glioma is influenced by immune-related signatures. Survival analysis further established the close correlation between network features and glioma prognosis. Subsequent experiments showed lncRNA RP11-770J1.4 regulates CTXN1 expression through hsa-miR-124-3p. Correlation analysis identified lncRNA RP11-770J1.4 was immune related, specifically involved in the cytosolic DNA sensing pathway. Downregulated lncRNA RP11-770J1.4 resulted in increased spontaneous gene expression of the cGAS-STING pathway. Single-cell RNA sequencing analysis, along with investigations in a glioblastoma stem cell model and patient sample analysis, demonstrated the predominant localization of CTXN1 within tumor cores rather than peripheral regions. Immunohistochemistry staining established a negative correlation between CTXN1 expression and infiltration of CD8+ T cells. In vivo, Ctxn1 knockdown in GL261 cells led to decreased tumor burden and improved survival while increasing infiltration of CD8+ T cells. These findings unveil novel insights into the lncRNA RP11-770J1.4-CTXN1 as a potential immune regulatory axis, highlighting its therapeutic implications for histologically LGGs.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

Charting the tumor microenvironment with spatial profiling technologies

In recent years technologies that can achieve readouts at cellular resolution such as single-cell RNA sequencing (scRNA-seq) have provided a comprehensive characterization of the cellular proportions and phenotypes that populate the tumor microenvironment (TME). However, because of the sample dissociation steps required by these protocols, they fail to capture information related to the intricate spatial context in which cells operate as well as their dense networks of interactions. Spatial profiling technologies have recently emerged as a valuable way to investigate the physical organization of cells crowding the TME in intact tissues. In this review we first discuss how spatial profiling technologies have propelled TME characterization, and then explore their potential to improve both diagnosis and prognosis for cancer patients in the clinic.

Single-cell profiling and zebrafish avatars reveal LGALS1 as immunomodulating target in glioblastoma

Glioblastoma (GBM) remains the most malignant primary brain tumor, with a median survival rarely exceeding 2 years. Tumor heterogeneity and an immunosuppressive microenvironment are key factors contributing to the poor response rates of current therapeutic approaches. GBM-associated macrophages (GAMs) often exhibit immunosuppressive features that promote tumor progression. However, their dynamic interactions with GBM tumor cells remain poorly understood. Here, we used patient-derived GBM stem cell cultures and combined single-cell RNA sequencing of GAM-GBM co-cultures and real-time in vivo monitoring of GAM-GBM interactions in orthotopic zebrafish xenograft models to provide insight into the cellular, molecular, and spatial heterogeneity. Our analyses revealed substantial heterogeneity across GBM patients in GBM-induced GAM polarization and the ability to attract and activate GAMs-features that correlated with patient survival. Differential gene expression analysis, immunohistochemistry on original tumor samples, and knock-out experiments in zebrafish subsequently identified LGALS1 as a primary regulator of immunosuppression. Overall, our work highlights that GAM-GBM interactions can be studied in a clinically relevant way using co-cultures and avatar models, while offering new opportunities to identify promising immune-modulating targets.

Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia

Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.

Toward the clinical development of synthetic immunity to cancer

Synthetic biology (synbio) tools, such as chimeric antigen receptors (CARs), have been designed to target, activate, and improve immune cell responses to tumors. These therapies have demonstrated an ability to cure patients with blood cancers. However, there are significant challenges to designing, testing, and efficiently translating these complex cell therapies for patients who do not respond or have immune refractory solid tumors. The rapid progress of synbio tools for cell therapy, particularly for cancer immunotherapy, is encouraging but our development process should be tailored to increase translational success. Particularly, next-generation cell therapies should be rooted in basic immunology, tested in more predictive preclinical models, engineered for potency with the right balance of safety, educated by clinical findings, and multi-faceted to combat a range of suppressive mechanisms. Here, we lay out five principles for engineering future cell therapies to increase the probability of clinical impact, and in the context of these principles, we provide an overview of the current state of synbio cell therapy design for cancer. Although these principles are anchored in engineering immune cells for cancer therapy, we posit that they can help guide translational synbio research for broad impact in other disease indications with high unmet need.

Brain macrophage development, diversity and dysregulation in health and disease

Brain macrophages include microglia in the parenchyma, border-associated macrophages in the meningeal-choroid plexus-perivascular space, and monocyte-derived macrophages that infiltrate the brain under various disease conditions. The vast heterogeneity of these cells has been elucidated over the last decade using revolutionary multiomics technologies. As such, we can now start to define these various macrophage populations according to their ontogeny and their diverse functional programs during brain development, homeostasis and disease pathogenesis. In this review, we first outline the critical roles played by brain macrophages during development and healthy aging. We then discuss how brain macrophages might undergo reprogramming and contribute to neurodegenerative disorders, autoimmune diseases, and glioma. Finally, we speculate about the most recent and ongoing discoveries that are prompting translational attempts to leverage brain macrophages as prognostic markers or therapeutic targets for diseases that affect the brain.

Cancer vaccine strategies for the treatment of diffusely infiltrating gliomas

Diffusely infiltrating gliomas - including glioblastoma (GBM), isocitrate dehydrogenase (IDH) mutant gliomas, and histone 3 (H3) altered gliomas - are primary brain tumors with an invariably fatal outcome. Despite advances in the understanding of their biology, standard, targeted and immune checkpoint inhibitor immunotherapies have proven ineffective in arresting their inexorable progression and associated morbidity and mortality. Recognizing the unique aspects of the immunogenicity of cancer cells, the last decade has seen the development and evaluation of vaccine-based therapies for the treatment of solid tumors, including gliomas. Here we review the current vaccine strategies for the treatment of GBM, IDH-mutant gliomas and diffuse midline glioma H3 K27M-altered. We discuss potential benefits and challenges of vaccine therapies in these specific patient populations.

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (αCD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict αCD40 treatment status and disease-free survival (DFS) following αCD40 therapy based upon TME features. Through downstream interpretation of the machine learning models' predictions, we found αCD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following αCD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of αCD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for αCD40-treated patients with PDAC.

The immune cell landscape of glioblastoma patients highlights a myeloid-enriched and immune suppressed microenvironment compared to metastatic brain tumors

Introduction

Brain metastases (BrM), which commonly arise in patients with melanoma, breast cancer and lung cancer, are associated with a poor clinical prognosis. In this context, the tumor microenvironment (TME) plays an important role since it either promotes or inhibits tumor progression. Our previous studies have characterized the immunosuppressive microenvironment of glioblastoma (GBM). The aim of this study is to compare the immune profiles of BrM and GBM in order to identify potential differences that may be exploited in their differential treatment.

Methods

Tumor and/or blood samples were taken from 20 BrM patients and 19 GBM patients. Multi-parametric flow cytometry was used to evaluate myeloid and lymphoid cells, as well as the expression of immune checkpoints in the TME and blood. In selected cases, the immunosuppressive ability of sorted myeloid cells was tested, and the ex vivo proliferation of myeloid, lymphoid and tumor cell populations was analyzed.

Results

High frequencies of myeloid cells dominated both the BrM and GBM landscapes, but a higher presence of tumor-associated macrophages was observed in GBM, while BrM were characterized by a significant presence of tumor-infiltrating lymphocytes. Exhaustion markers were highly expressed in all T cells from both primary and metastatic brain tumors. Ex vivo analysis of the cell cycle of a single sample of a BrM and of a GBM revealed subsets of proliferating tumor cells and blood-derived macrophages, but quiescent resident microglial cells and few proliferating lymphocytes. Macrophages sorted from a single lung BrM exhibited a strong immunosuppressive activity, as previously shown for primary GBM. Finally, a significant expansion of some myeloid cell subsets was observed in the blood of both GBM and BrM patients.

Discussion

Our results define the main characteristics of the immune profile of BrM and GBM, which are distinguished by different levels of immunosuppressive myeloid cells and lymphocytes devoid of effector function. Understanding the role of the different cells in establishing the metastatic setting is critical for improving the therapeutic efficacy of new targeted immunotherapy strategies.

Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging

The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment.​.

From signalling pathways to targeted therapies: unravelling glioblastoma's secrets and harnessing two decades of progress

Glioblastoma, a rare, and highly lethal form of brain cancer, poses significant challenges in terms of therapeutic resistance, and poor survival rates for both adult and paediatric patients alike. Despite advancements in brain cancer research driven by a technological revolution, translating our understanding of glioblastoma pathogenesis into improved clinical outcomes remains a critical unmet need. This review emphasises the intricate role of receptor tyrosine kinase signalling pathways, epigenetic mechanisms, and metabolic functions in glioblastoma tumourigenesis and therapeutic resistance. We also discuss the extensive efforts over the past two decades that have explored targeted therapies against these pathways. Emerging therapeutic approaches, such as antibody-toxin conjugates or CAR T cell therapies, offer potential by specifically targeting proteins on the glioblastoma cell surface. Combination strategies incorporating protein-targeted therapy and immune-based therapies demonstrate great promise for future clinical research. Moreover, gaining insights into the role of cell-of-origin in glioblastoma treatment response holds the potential to advance precision medicine approaches. Addressing these challenges is crucial to improving outcomes for glioblastoma patients and moving towards more effective precision therapies.

Targeting the dendritic cell-T cell axis to develop effective immunotherapies for glioblastoma

Glioblastoma is an aggressive primary brain tumor that has seen few advances in treatments for over 20 years. In response to this desperate clinical need, multiple immunotherapy strategies are under development, including CAR-T cells, immune checkpoint inhibitors, oncolytic viruses and dendritic cell vaccines, although these approaches are yet to yield significant clinical benefit. Potential reasons for the lack of success so far include the immunosuppressive tumor microenvironment, the blood-brain barrier, and systemic changes to the immune system driven by both the tumor and its treatment. Furthermore, while T cells are essential effector cells for tumor control, dendritic cells play an equally important role in T cell activation, and emerging evidence suggests the dendritic cell compartment may be deeply compromised in glioblastoma patients. In this review, we describe the immunotherapy approaches currently under development for glioblastoma and the challenges faced, with a particular emphasis on the critical role of the dendritic cell-T cell axis. We suggest a number of strategies that could be used to boost dendritic cell number and function and propose that the use of these in combination with T cell-targeting strategies could lead to successful tumor control.

Agent-Based Modelling Reveals the Role of the Tumor Microenvironment on the Short-Term Success of Combination Temozolomide/Immune Checkpoint Blockade to Treat Glioblastoma

Glioblastoma is the most common and deadly primary brain tumor in adults. All glioblastoma patients receiving standard-of-care surgery-radiotherapy-chemotherapy (i.e., temozolomide (TMZ)) recur, with an average survival time of only 15 months. New approaches to the treatment of glioblastoma, including immune checkpoint blockade and oncolytic viruses, offer the possibility of improving glioblastoma outcomes and have as such been under intense study. Unfortunately, these treatment modalities have thus far failed to achieve approval. Recently, in an attempt to bolster efficacy and improve patient outcomes, regimens combining chemotherapy and immune checkpoint inhibitors have been tested in trials. Unfortunately, these efforts have not resulted in significant increases to patient survival. To better understand the various factors impacting treatment outcomes of combined TMZ and immune checkpoint blockade, we developed a systems-level, computational model that describes the interplay between glioblastoma, immune, and stromal cells with this combination treatment. Initializing our model to spatial resection patient samples labeled using imaging mass cytometry, our model's predictions show how the localization of glioblastoma cells, influence therapeutic success. We further validated these predictions in samples of brain metastases from patients given they generally respond better to checkpoint blockade compared with primary glioblastoma. Ultimately, our model provides novel insights into the mechanisms of therapeutic success of immune checkpoint inhibitors in brain tumors and delineates strategies to translate combination immunotherapy regimens more effectively into the clinic. SIGNIFICANCE STATEMENT: Extending survival times for glioblastoma patients remains a critical challenge. Although immunotherapies in combination with chemotherapy hold promise, clinical trials have not shown much success. Here, systems models calibrated to and validated against patient samples can improve preclinical and clinical studies by shedding light on the factors distinguishing responses/failures. By initializing our model with imaging mass cytometry visualization of patient samples, we elucidate how factors such as localization of glioblastoma cells and CD8+ T cell infiltration impact treatment outcomes.

Targeting of endothelial cells in brain tumours

BACKGROUND

Aggressive brain tumours, whether primary gliomas or secondary metastases, are characterised by hypervascularisation and are fatal. Recent research has emphasised the crucial involvement of endothelial cells (ECs) in all brain tumour genesis and development events, with various patterns and underlying mechanisms identified.

MAIN BODY

Here, we highlight recent advances in knowledge about the contributions of ECs to brain tumour development, providing a comprehensive summary including descriptions of interactions between ECs and tumour cells, the heterogeneity of ECs and new models for research on ECs in brain malignancies. We also discuss prospects for EC targeting in novel therapeutic approaches.

CONCLUSION

Interventions targeting ECs, as an adjunct to other therapies (e.g. immunotherapies, molecular-targeted therapies), have shown promising clinical efficacy due to the high degree of vascularisation in brain tumours. Developing precise strategies to target tumour-associated vessels based on the heterogeneity of ECs is expected to improve anti-vascular efficacy.

Tissue-resident macrophages - early passengers or drivers in the tumor niche?

Macrophages within the tumor microenvironment of solid tumors and metastasis are heterogeneous populations, which contribute to diverse steps of tumorigenesis. Tumor-associated macrophages (TAMs) can either derive from circulation-derived monocytes or tissue-resident macrophages (TRMs). In health, TRMs populate the majority of tissues, orchestrating critical homeostatic and reparative functions. While TRM-specific functions in tumor initiation and progression remain unclear, recent studies have revealed that TRMs are a significant source of TAMs in both mouse and human cancers, where they closely resemble gene signatures of their normal, organ-specific TRM counterparts. In this review, we highlight recent advances toward systematically understanding the role of TRMs as an important TAM subset and opportunities how this macrophage population could be exploited for therapeutical targeting strategies.

Decoding Cancer Evolution: Integrating Genetic and Non-Genetic Insights

The development of cancer begins with cells transitioning from their multicellular nature to a state akin to unicellular organisms. This shift leads to a breakdown in the crucial regulators inherent to multicellularity, resulting in the emergence of diverse cancer cell subpopulations that have enhanced adaptability. The presence of different cell subpopulations within a tumour, known as intratumoural heterogeneity (ITH), poses challenges for cancer treatment. In this review, we delve into the dynamics of the shift from multicellularity to unicellularity during cancer onset and progression. We highlight the role of genetic and non-genetic factors, as well as tumour microenvironment, in promoting ITH and cancer evolution. Additionally, we shed light on the latest advancements in omics technologies that allow for in-depth analysis of tumours at the single-cell level and their spatial organization within the tissue. Obtaining such detailed information is crucial for deepening our understanding of the diverse evolutionary paths of cancer, allowing for the development of effective therapies targeting the key drivers of cancer evolution.

The diversity and dynamics of tumor-associated macrophages in recurrent glioblastoma

Despite tremendous efforts to exploit effective therapeutic strategies, most glioblastoma (GBM) inevitably relapse and become resistant to therapies, including radiotherapy and immunotherapy. The tumor microenvironment (TME) of recurrent GBM (rGBM) is highly immunosuppressive, dominated by tumor-associated macrophages (TAMs). TAMs consist of tissue-resident microglia and monocyte-derived macrophages (MDMs), which are essential for favoring tumor growth, invasion, angiogenesis, immune suppression, and therapeutic resistance; however, restricted by the absence of potent methods, the heterogeneity and plasticity of TAMs in rGBM remain incompletely investigated. Recent application of single-cell technologies, such as single-cell RNA-sequencing has enabled us to decipher the unforeseen diversity and dynamics of TAMs and to identify new subsets of TAMs which regulate anti-tumor immunity. Here, we first review hallmarks of the TME, progress and challenges of immunotherapy, and the biology of TAMs in the context of rGBM, including their origins, categories, and functions. Next, from a single-cell perspective, we highlight recent findings regarding the distinctions between tissue-resident microglia and MDMs, the identification and characterization of specific TAM subsets, and the dynamic alterations of TAMs during tumor progression and treatment. Last, we briefly discuss the potential of TAM-targeted strategies for combination immunotherapy in rGBM. We anticipate the comprehensive understanding of the diversity and dynamics of TAMs in rGBM will shed light on further improvement of immunotherapeutic efficacy in rGBM.

Clinical and translational relevance of intratumor heterogeneity

Intratumor heterogeneity (ITH) is a driver of tumor evolution and a main cause of therapeutic resistance. Despite its importance, measures of ITH are still not incorporated into clinical practice. Consequently, standard treatment is frequently ineffective for patients with heterogeneous tumors as changes to treatment regimens are made only after recurrence and disease progression. More effective combination therapies require a mechanistic understanding of ITH and ways to assess it in clinical samples. The growth of technologies enabling the spatially intact analysis of tumors at the single-cell level and the development of sophisticated preclinical models give us hope that ITH will not simply be used as a predictor of a poor outcome but will guide treatment decisions from diagnosis through treatment.

Construction and validation of an immune infiltration-related risk model for predicting prognosis and immunotherapy response in low grade glioma

BACKGROUND

Low grade glioma (LGG) is considered a heterogeneous tumor with highly variable survival and limited efficacy of immunotherapy. To identify high-risk subsets and apply immunotherapy effectively in LGG, the status and function of immune infiltration in the glioma microenvironment must be explored.

METHODS

Four independent glioma cohorts comprising 1,853 patients were enrolled for bioinformatics analysis. We used ConsensusClusterPlus to cluster patients into four different immune subtypes based on immune infiltration. The immune-infiltration signature (IIS) was constructed by LASSO regression analysis. Somatic mutation and copy number variation (CNV) analyses were performed to explore genomic and transcriptomic traits in the high- and low- risk groups. The correlation between response to programmed cell death 1 (PD-1) blockade and the IIS risk score was confirmed in an in vivo glioma model.

RESULTS

Patients were clustered into four different immune subtypes based on immune infiltration, and the high immune infiltration subtype was associated with worse survival in LGG. The high immune infiltration subtype had stronger inflammatory response, immune response and immune cell chemotaxis. The IIS, consisting of EMP3, IQGAP2, METTL7B, SLC1A6 and TNFRSF11B, could predict LGG malignant progression, which was validated with internal clinical samples. M2 macrophage infiltration positively correlated with the IIS risk score. The high-risk group had significantly more somatic mutations and CNVs. The IIS risk score was related to immunomodulatory molecules and could predict immunotherapy clinical benefit. In vivo, immunotherapy-sensitive glioma model exhibited higher IIS risk score and more infiltration of immune cells, especially M2 macrophages. The IIS risk score was decreased in an immunotherapy-sensitive glioma model after anti-PD1 immunotherapy.

CONCLUSION

Different immune subtypes of LGG had unique immune cell infiltration characteristics, and the high immune infiltration subtype was associated with immunosuppressive signaling pathways. A novel IIS prognostic model based on immune infiltration status was constructed for immunophenotypic classification, risk stratification, prognostication and immunotherapy response prediction in LGG.

Elucidating the Role of Pyroptosis in Lower-Grade Glioma: Development of a Novel Scoring System to Enhance Personalized Therapeutic Approaches

Pyroptosis, an orchestrated cellular death pathway, has gained attention due to its role in the pathophysiology and evolution of numerous malignancies. Despite this, no robust quantitative measure of pyroptosis activity in lower-grade glioma (LGG) exists currently. We scrutinized the transcriptomic data of LGG specimens acquired from TCGA and CGGA repositories, juxtaposed with the expression patterns of healthy brain tissues from the GTEx database. A register of pyroptosis-associated genes was extracted from the GSEA database. Utilizing unsupervised clustering algorithms on the expression patterns of these genes, we stratified LGG samples into unique subgroups. We implemented the Boruta machine learning algorithm to discern representative variables for each pyroptosis subtype and applied principal component analysis (PCA) to condense the dimensionality of the feature gene expression data, which led to the formulation of a pyroptosis scoring system (P score) to estimate pyroptosis activity in LGG. Furthermore, we affirmed the capacity of the P score to discriminate diverse cell subpopulations within a single-cell database and explored the correlations between the P score and clinical attributes, prognostic implications, and the tumor immune microenvironment in LGG. We identified three distinctive pyroptosis patterns with significant correlations to patient survival, clinicopathological properties, and characteristics of the tumor immune microenvironment (TIME). Two gene clusters, associated with unique prognostic and TIME attributes, emerged from differentially expressed genes (DEGs) across the pyroptosis patterns. The P score was formulated and authenticated as an autonomous prognostic determinant for overall survival in the TCGA and CGGA cohorts. Additionally, the P score demonstrated its competency to quantitatively represent pyroptosis activity across different cellular subpopulations in single-cell data. Notably, the P score in LGG was found to be indicative of tumor stemness and could serve as a predictive biomarker for the efficacy of temozolomide treatment and immunotherapy, underscoring its potential clinical utility. Our investigation pioneers a novel pyroptosis-centric scoring system with significant prognostic implications. The P score holds promise as a potential predictive biomarker for the response to chemotherapy and immunotherapy, facilitating the development of personalized therapeutic approaches in LGG patients.

Dissecting Intra-tumor Heterogeneity in the Glioblastoma Microenvironment Using Fluorescence-Guided Multiple Sampling

The treatment of the most aggressive primary brain tumor in adults, glioblastoma (GBM), is challenging due to its heterogeneous nature, invasive potential, and poor response to chemo- and radiotherapy. As a result, GBM inevitably recurs and only a few patients survive 5 years post-diagnosis. GBM is characterized by extensive phenotypic and genetic heterogeneity, creating a diversified genetic landscape and a network of biological interactions between subclones, ultimately promoting tumor growth and therapeutic resistance. This includes spatial and temporal changes in the tumor microenvironment, which influence cellular and molecular programs in GBM and therapeutic responses. However, dissecting phenotypic and genetic heterogeneity at spatial and temporal levels is extremely challenging, and the dynamics of the GBM microenvironment cannot be captured by analysis of a single tumor sample. In this review, we discuss the current research on GBM heterogeneity, in particular, the utility and potential applications of fluorescence-guided multiple sampling to dissect phenotypic and genetic intra-tumor heterogeneity in the GBM microenvironment, identify tumor and non-tumor cell interactions and novel therapeutic targets in areas that are key for tumor growth and recurrence, and improve the molecular classification of GBM.

Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Spatial cellular architecture predicts prognosis in glioblastoma

Intra-tumoral heterogeneity and cell-state plasticity are key drivers for the therapeutic resistance of glioblastoma. Here, we investigate the association between spatial cellular organization and glioblastoma prognosis. Leveraging single-cell RNA-seq and spatial transcriptomics data, we develop a deep learning model to predict transcriptional subtypes of glioblastoma cells from histology images. Employing this model, we phenotypically analyze 40 million tissue spots from 410 patients and identify consistent associations between tumor architecture and prognosis across two independent cohorts. Patients with poor prognosis exhibit higher proportions of tumor cells expressing a hypoxia-induced transcriptional program. Furthermore, a clustering pattern of astrocyte-like tumor cells is associated with worse prognosis, while dispersion and connection of the astrocytes with other transcriptional subtypes correlate with decreased risk. To validate these results, we develop a separate deep learning model that utilizes histology images to predict prognosis. Applying this model to spatial transcriptomics data reveal survival-associated regional gene expression programs. Overall, our study presents a scalable approach to unravel the transcriptional heterogeneity of glioblastoma and establishes a critical connection between spatial cellular architecture and clinical outcomes.

Glioblastoma modeling with 3D organoids: progress and challenges

Glioblastoma (GBM) is the most aggressive adult primary brain tumor with nearly universal treatment resistance and recurrence. The mainstay of therapy remains maximal safe surgical resection followed by concurrent radiation therapy and temozolomide chemotherapy. Despite intensive investigation, alternative treatment options, such as immunotherapy or targeted molecular therapy, have yielded limited success to achieve long-term remission. This difficulty is partly due to the lack of pre-clinical models that fully recapitulate the intratumoral and intertumoral heterogeneity of GBM and the complex tumor microenvironment. Recently, GBM 3D organoids originating from resected patient tumors, genetic manipulation of induced pluripotent stem cell (iPSC)-derived brain organoids and bio-printing or fusion with non-malignant tissues have emerged as novel culture systems to portray the biology of GBM. Here, we highlight several methodologies for generating GBM organoids and discuss insights gained using such organoid models compared to classic modeling approaches using cell lines and xenografts. We also outline limitations of current GBM 3D organoids, most notably the difficulty retaining the tumor microenvironment, and discuss current efforts for improvements. Finally, we propose potential applications of organoid models for a deeper mechanistic understanding of GBM and therapeutic development.

Targeting cGAS/STING signaling-mediated myeloid immune cell dysfunction in TIME

Myeloid immune cells (MICs) are potent innate immune cells serving as first responders to invading pathogens and internal changes to cellular homeostasis. Cancer is a stage of altered cellular homeostasis that can originate in response to different pathogens, chemical carcinogens, and internal genetic/epigenetic changes. MICs express several pattern recognition receptors (PRRs) on their membranes, cytosol, and organelles, recognizing systemic, tissue, and organ-specific altered homeostasis. cGAS/STING signaling is a cytosolic PRR system for identifying cytosolic double-stranded DNA (dsDNA) in a sequence-independent but size-dependent manner. The longer the cytosolic dsDNA size, the stronger the cGAS/STING signaling activation with increased type 1 interferon (IFN) and NF-κB-dependent cytokines and chemokines' generation. The present article discusses tumor-supportive changes occurring in the tumor microenvironment (TME) or tumor immune microenvironment (TIME) MICs, specifically emphasizing cGAS/STING signaling-dependent alteration. The article further discusses utilizing MIC-specific cGAS/STING signaling modulation as critical tumor immunotherapy to alter TIME.

Selective oxidative protection leads to tissue topological changes orchestrated by macrophage during ulcerative colitis

Ulcerative colitis is a chronic inflammatory bowel disorder with cellular heterogeneity. To understand the composition and spatial changes of the ulcerative colitis ecosystem, here we use imaging mass cytometry and single-cell RNA sequencing to depict the single-cell landscape of the human colon ecosystem. We find tissue topological changes featured with macrophage disappearance reaction in the ulcerative colitis region, occurring only for tissue-resident macrophages. Reactive oxygen species levels are higher in the ulcerative colitis region, but reactive oxygen species scavenging enzyme SOD2 is barely detected in resident macrophages, resulting in distinct reactive oxygen species vulnerability for inflammatory macrophages and resident macrophages. Inflammatory macrophages replace resident macrophages and cause a spatial shift of TNF production during ulcerative colitis via a cytokine production network formed with T and B cells. Our study suggests components of a mechanism for the observed macrophage disappearance reaction of resident macrophages, providing mechanistic hints for macrophage disappearance reaction in other inflammation or infection situations.

Recent advancements in tumour microenvironment landscaping for target selection and response prediction in immune checkpoint therapies achieved through spatial protein multiplexing analysis

Immune checkpoint therapies have significantly advanced cancer treatment. Nevertheless, the high costs and potential adverse effects associated with these therapies highlight the need for better predictive biomarkers to identify patients who are most likely to benefit from treatment. Unfortunately, the existing biomarkers are insufficient to identify such patients. New high-dimensional spatial technologies have emerged as a valuable tool for discovering novel biomarkers by analysing multiple protein markers at a single-cell resolution in tissue samples. These technologies provide a more comprehensive map of tissue composition, cell functionality, and interactions between different cell types in the tumour microenvironment. In this review, we provide an overview of how spatial protein-based multiplexing technologies have fuelled biomarker discovery and advanced the field of immunotherapy. In particular, we will focus on how these technologies contributed to (i) characterise the tumour microenvironment, (ii) understand the role of tumour heterogeneity, (iii) study the interplay of the immune microenvironment and tumour progression, (iv) discover biomarkers for immune checkpoint therapies (v) suggest novel therapeutic strategies.

Terpinen-4-ol Induces Ferroptosis of Glioma Cells via Downregulating JUN Proto-Oncogene

According to previous research, turmeric seeds exhibit anti-inflammatory, anti-malignancy, and anti-aging properties due to an abundance of terpinen-4-ol (T4O). Although it is still unclear how T4O works on glioma cells, limited data exist regarding its specific effects. In order to determine whether or not glioma cell lines U251, U87, and LN229 are viable, CCK8 was used as an assay and a colony formation assay was performed using different concentrations of T4O (0, 1, 2, and 4 μM). The effect of T4O on the proliferation of glioma cell line U251 was detected through the subcutaneous implantation of the tumor model. Through high-throughput sequencing, a bioinformatic analysis, and real-time quantitative polymerase chain reactions, we identified the key signaling pathways and targets of T4O. Finally, for the measurement of the cellular ferroptosis levels, we examined the relationship between T4O, ferroptosis, and JUN and the malignant biological properties of glioma cells. T4O significantly inhibited glioma cell growth and colony formation and induced ferroptosis in the glioma cells. T4O inhibited the subcutaneous tumor proliferation of the glioma cells in vivo. T4O suppressed JUN transcription and significantly reduced its expression in the glioma cells. The T4O treatment inhibited GPX4 transcription through JUN. The overexpression of JUN suppressed ferroptosis in the cells rescued through T4O treatment. Taken together, our data suggest that the natural product T4O exerts its anti-cancer effects by inducing JUN/GPX4-dependent ferroptosis and inhibiting cell proliferation, and T4O will hope-fully serve as a prospective compound for glioma treatment.

Deciphering the tumour immune microenvironment cell by cell

Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie in the tumour microenvironment (TME). A wealth of data demonstrates the huge impact of the TME on ICI response and resistance. However, these data also reveal the complexity of the TME composition including the spatiotemporal interactions between different cell types and their dynamic changes in response to ICIs. Here, we briefly review some of the modalities that sculpt the TME, in particular the metabolic milieu, hypoxia and the role of cancer-associated fibroblasts. We then discuss recent approaches to dissect the TME with a focus on single-cell RNA sequencing, spatial transcriptomics and spatial proteomics. We also discuss some of the clinically relevant findings these multi-modal analyses have yielded.

The strange Microenvironment of Glioblastoma

Glioblastoma (GB) is the most common and aggressive primary brain tumor, with poor patient survival and lack of effective therapies. Late advances trying to decipher the composition of the GB tumor microenvironment (TME) emphasized its role in tumor progression and potentialized it as a therapeutic target. Many components participate critically to tumor development and expansion such as blood vessels, immune cells or components of the nervous system. Dysmorphic tumor vasculature brings challenges to optimal delivery of cytotoxic agents currently used in clinics. Also, massive infiltration of immunosuppressive myeloid cells and limited recruitment of T cells limits the success of conventional immunotherapies. Neuronal input seems also be required for tumor expansion. In this review, we provide a comprehensive report of vascular and immune component of the GB TME and their cross talk during GB progression.

Spatial landscape of the tumor immune microenvironment

Two recent studies published in Nature by Karimi et al. and Sorin et al. applied multiplexed imaging mass cytometry (IMC) to characterize the single-cell tumor immune landscapes covering millions of cells in brain tumors and lung adenocarcinomas (LUAD). They identified specific cell subtypes and cell-cell interactions that correlate with distinct clinical outcomes, thus providing valuable insights into tumor biology and prognostic prediction based on spatial architecture.

Harnessing type I interferon-mediated immunity to target malignant brain tumors

Type I interferons have long been appreciated as a cytokine family that regulates antiviral immunity. Recently, their role in eliciting antitumor immune responses has gained increasing attention. Within the immunosuppressive tumor microenvironment (TME), interferons stimulate tumor-infiltrating lymphocytes to promote immune clearance and essentially reshape a "cold" TME into an immune-activating "hot" TME. In this review, we focus on gliomas, with an emphasis on malignant glioblastoma, as these brain tumors possess a highly invasive and heterogenous brain TME. We address how type I interferons regulate antitumor immune responses against malignant gliomas and reshape the overall immune landscape of the brain TME. Furthermore, we discuss how these findings can translate into future immunotherapies targeting brain tumors in general.

Interdependencies of the Neuronal, Immune and Tumor Microenvironment in Gliomas

Gliomas are the most common primary brain malignancy and are universally fatal. Despite significant breakthrough in understanding tumor biology, treatment breakthroughs have been limited. There is a growing appreciation that major limitations on effective treatment are related to the unique and highly complex glioma tumor microenvironment (TME). The TME consists of multiple different cell types, broadly categorized into tumoral, immune and non-tumoral, non-immune cells. Each group provides significant influence on the others, generating a pro-tumor dynamic with significant immunosuppression. In addition, glioma cells are highly heterogenous with various molecular distinctions on the cellular level. These variations, in turn, lead to their own unique influence on the TME. To develop future treatments, an understanding of this complex TME interplay is needed. To this end, we describe the TME in adult gliomas through interactions between its various components and through various glioma molecular phenotypes.

The role of eIF4F-driven mRNA translation in regulating the tumour microenvironment

Cells can rapidly adjust their proteomes in dynamic environments by regulating mRNA translation. There is mounting evidence that dysregulation of mRNA translation supports the survival and adaptation of cancer cells, which has stimulated clinical interest in targeting elements of the translation machinery and, in particular, components of the eukaryotic initiation factor 4F (eIF4F) complex such as eIF4E. However, the effect of targeting mRNA translation on infiltrating immune cells and stromal cells in the tumour microenvironment (TME) has, until recently, remained unexplored. In this Perspective article, we discuss how eIF4F-sensitive mRNA translation controls the phenotypes of key non-transformed cells in the TME, with an emphasis on the underlying therapeutic implications of targeting eIF4F in cancer. As eIF4F-targeting agents are in clinical trials, we propose that a broader understanding of their effect on gene expression in the TME will reveal unappreciated therapeutic vulnerabilities that could be used to improve the efficacy of existing cancer therapies.

Genomic Analysis of Human Brain Metastases Treated with Stereotactic Radiosurgery Under the Phase-II Clinical Trial (NCT03398694) Reveals DNA Damage Repair at the Peripheral Tumor Edge

Stereotactic Radiosurgery (SRS) is one of the leading treatment modalities for oligo brain metastasis (BM), however no comprehensive genomic data assessing the effect of radiation on BM in humans exist. Leveraging a unique opportunity, as part of the clinical trial (NCT03398694), we collected post-SRS, delivered via Gamma-knife or LINAC, tumor samples from core and peripheral-edges of the resected tumor to characterize the genomic effects of overall SRS as well as the SRS delivery modality. Using these rare patient samples, we show that SRS results in significant genomic changes at DNA and RNA levels throughout the tumor. Mutations and expression profiles of peripheral tumor samples indicated interaction with surrounding brain tissue as well as elevated DNA damage repair. Central samples show GSEA enrichment for cellular apoptosis while peripheral samples carried an increase in tumor suppressor mutations. There are significant differences in the transcriptomic profile at the periphery between Gamma-knife vs LINAC.

Macrophages at the interface of the co-evolving cancer ecosystem

Macrophages are versatile and heterogeneous innate immune cells undertaking central functions in balancing immune responses and tissue repair to maintain homeostasis. This plasticity, once co-opted by malignant outgrowth, orchestrates manifold reciprocal interactions within the tumor microenvironment, fueling the evolution of the cancer ecosystem. Here, we review the multilayered sources of influence that jointly underpin and longitudinally shape tumor-associated macrophage (TAM) phenotypic states in solid neoplasms. We discuss how, in response to these signals, TAMs steer tumor evolution in the context of natural selection, biological dispersion, and treatment resistance. A number of research frontiers to be tackled are laid down in this review to therapeutically exploit the complex roles of TAMs in cancer. Building upon knowledge obtained from currently applied TAM-targeting strategies and using next generation technologies, we propose conceptual advances and novel therapeutic avenues to rewire TAM multifaceted regulation of the co-evolving cancer ecosystem.

Dissecting the tumor microenvironment of epigenetically driven gliomas: Opportunities for single-cell and spatial multiomics

Malignant gliomas are incurable brain neoplasms with dismal prognoses and near-universal fatality, with minimal therapeutic progress despite billions of dollars invested in research and clinical trials over the last 2 decades. Many glioma studies have utilized disparate histologic and genomic platforms to characterize the stunning genomic, transcriptomic, and immunologic heterogeneity found in gliomas. Single-cell and spatial omics technologies enable unprecedented characterization of heterogeneity in solid malignancies and provide a granular annotation of transcriptional, epigenetic, and microenvironmental states with limited resected tissue. Heterogeneity in gliomas may be defined, at the broadest levels, by tumors ostensibly driven by epigenetic alterations (IDH- and histone-mutant) versus non-epigenetic tumors (IDH-wild type). Epigenetically driven tumors are defined by remarkable transcriptional programs, immunologically distinct microenvironments, and incompletely understood topography (unique cellular neighborhoods and cell-cell interactions). Thus, these tumors are the ideal substrate for single-cell multiomic technologies to disentangle the complex intra-tumoral features, including differentiation trajectories, tumor-immune cell interactions, and chromatin dysregulation. The current review summarizes the applications of single-cell multiomics to existing datasets of epigenetically driven glioma. More importantly, we discuss future capabilities and applications of novel multiomic strategies to answer outstanding questions, enable the development of potent therapeutic strategies, and improve personalized diagnostics and treatment via digital pathology.