Single cell spatial analysis reveals the topology of immunomodulatory purinergic signaling in glioblastoma

Cellular diversity through space and time: adding new dimensions to GBM therapeutic development

The current median survival for glioblastoma (GBM) patients is only about 16 months, with many patients succumbing to the disease in just a matter of months, making it the most common and aggressive primary brain cancer in adults. This poor outcome is, in part, due to the lack of new treatment options with only one FDA-approved treatment in the last decade. Advances in sequencing techniques and transcriptomic analyses have revealed a vast degree of heterogeneity in GBM, from inter-patient diversity to intra-tumoral cellular variability. These cutting-edge approaches are providing new molecular insights highlighting a critical role for the tumor microenvironment (TME) as a driver of cellular plasticity and phenotypic heterogeneity. With this expanded molecular toolbox, the influence of TME factors, including endogenous (e.g., oxygen and nutrient availability and interactions with non-malignant cells) and iatrogenically induced (e.g., post-therapeutic intervention) stimuli, on tumor cell states can be explored to a greater depth. There exists a critical need for interrogating the temporal and spatial aspects of patient tumors at a high, cell-level resolution to identify therapeutically targetable states, interactions and mechanisms. In this review, we discuss advancements in our understanding of spatiotemporal diversity in GBM with an emphasis on the influence of hypoxia and immune cell interactions on tumor cell heterogeneity. Additionally, we describe specific high-resolution spatially resolved methodologies and their potential to expand the impact of pre-clinical GBM studies. Finally, we highlight clinical attempts at targeting hypoxia- and immune-related mechanisms of malignancy and the potential therapeutic opportunities afforded by single-cell and spatial exploration of GBM patient specimens.

Mass spectrometry imaging methods for visualizing tumor heterogeneity

Profiling spatial distributions of lipids, metabolites, and proteins in tumors can reveal unique cellular microenvironments and provide molecular evidence for cancer cell dysfunction and proliferation. Mass spectrometry imaging (MSI) is a label-free technique that can be used to map biomolecules in tumors in situ. Here, we discuss current progress in applying MSI to uncover molecular heterogeneity in tumors. First, the analytical strategies to profile small molecules and proteins are outlined, and current methods for multimodal imaging to maximize biological information are highlighted. Second, we present and summarize biological insights obtained by MSI of tumor tissue. Finally, we discuss important considerations for designing MSI experiments and several current analytical challenges.

Glioblastoma-Associated Mesenchymal Stem/Stromal Cells and Cancer-Associated Fibroblasts: Partners in Crime?

Tumor-associated mesenchymal stem/stromal cells (TA-MSCs) have been recognized as attractive therapeutic targets in several cancer types, due to their ability to enhance tumor growth and angiogenesis and their contribution to an immunosuppressive tumor microenvironment (TME). In glioblastoma (GB), mesenchymal stem cells (MSCs) seem to be recruited to the tumor site, where they differentiate into glioblastoma-associated mesenchymal stem/stromal cells (GA-MSCs) under the influence of tumor cells and the TME. GA-MSCs are reported to exert important protumoral functions, such as promoting tumor growth and invasion, increasing angiogenesis, stimulating glioblastoma stem cell (GSC) proliferation and stemness, mediating resistance to therapy and contributing to an immunosuppressive TME. Moreover, they could act as precursor cells for cancer-associated fibroblasts (CAFs), which have recently been identified in GB. In this review, we provide an overview of the different functions exerted by GA-MSCs and CAFs and the current knowledge on the relationship between these cell types. Increasing our understanding of the interactions and signaling pathways in relevant models might contribute to future regimens targeting GA-MSCs and GB-associated CAFs to inhibit tumor growth and render the TME less immunosuppressive.

Characterization of purinergic signaling in tumor-infiltrating lymphocytes from lower- and high-grade gliomas

Malignant gliomas are highly heterogeneous glia-derived tumors that present an aggressive and invasive nature, with a dismal prognosis. The multi-dimensional interactions between glioma cells and other tumor microenvironment (TME) non-tumoral components constitute a challenge to finding successful treatment strategies. Several molecules, such as extracellular purines, participate in signaling events and support the immunosuppressive TME of glioma patients. The purinergic signaling and the ectoenzymes network involved in the metabolism of these extracellular nucleotides are still unexplored in the glioma TME, especially in lower-grade gliomas (LGG). Also, differences between IDH-mutant (IDH-Mut) versus wild-type (IDH-WT) gliomas are still unknown in this context. For the first time, to our knowledge, this study characterizes the TME of LGG, high-grade gliomas (HGG) IDH-Mut, and HGG IDH-WT patients regarding purinergic ectoenzymes and P1 receptors, focusing on tumor-infiltrating lymphocytes. Here, we show that ectoenzymes from both canonical and non-canonical pathways are increased in the TME when compared to the peripheral blood. We hypothesize this enhancement supports extracellular adenosine generation, hence increasing TME immunosuppression.

Role of the Microenvironment in Glioma Pathogenesis

Gliomas are a diverse group of primary central nervous system tumors that affect both children and adults. Recent studies have revealed a dynamic cross talk that occurs between glioma cells and components of their microenvironment, including neurons, astrocytes, immune cells, and the extracellular matrix. This cross talk regulates fundamental aspects of glioma development and growth. In this review, we discuss recent discoveries about the impact of these interactions on gliomas and highlight how tumor cells actively remodel their microenvironment to promote disease. These studies provide a better understanding of the interactions in the microenvironment that are important in gliomas, offer insight into the cross talk that occurs, and identify potential therapeutic vulnerabilities that can be utilized to improve clinical outcomes.

Intercellular crosstalk shapes purinergic metabolism and signaling in cancer cells

CD73-derived adenosine suppresses anti-cancer immunity, and CD73 inhibitors are currently evaluated in several clinical trials. Here, we have assessed enzyme kinetics of all key purinergic ectoenzymes in five cancer cell lines (Hodgkin lymphoma, multiple myeloma, pancreas adenocarcinoma, urinary bladder carcinoma, and glioblastoma) under normoxia and hypoxia. We found that adenosine metabolism varied considerably between individual cancer types. All cell lines investigated exhibited high ecto-adenosine deaminase (ADA) activity, which critically influenced the kinetics of adenosine accumulation. Combining kinetics data with single-cell RNA sequencing data on myeloma and glioblastoma cancerous tissue revealed that purine metabolism is not homogeneously organized, but it differs in a cancer type-specific fashion between malignant cells, stromal cells, and immune cells. Since purine metabolism in cancerous tissue is most likely spatially heterogeneous and differs between the various cell types, diffusion distances in the microenvironment as well as ADA activity may be important variables that influence the level of bioactive adenosine.

Impact of spatial metabolomics on immune-microenvironment in oral cancer prognosis: a clinical report

MALDI imaging for metabolites and immunohistochemistry for 38 immune markers was used to characterize the spatial biology of 2 primary oral tumours, one from a patient with an early recurrence (Tumour R), and the other from a patient with no recurrence 2 years after treatment completion (Tumour NR). Tumour R had an increased purine nucleotide metabolism in different regions of tumour and adenosine-mediated suppression of immune cells compared to Tumour NR. The differentially expressed markers in the different spatial locations in tumour R were CD33, CD163, TGF-β, COX2, PD-L1, CD8 and CD20. These results suggest that altered tumour metabolomics concomitant with a modified immune microenvironment could be a potential marker of recurrence.

'Slicing' glioblastoma drivers with the Swiss cheese model

The Swiss cheese model is used to assess risks and explain accidents in a variety of industries. This model can be applied to dissect the homeostatic mechanisms whose cumulative dysregulation contributes to disease states, including cancer. Using glioblastoma (GBM) as an exemplar, we discuss how specific protumorigenic mechanisms collectively drive disease by affecting genomic integrity, epigenetic regulation, metabolic homeostasis, and antitumor immunity. We further highlight how host factors, such as hormonal differences and aging, impact this process, and the interplay between these 'system failures' that enable tumor progression and foster therapeutic resistance. Finally, we examine therapies that consider the interactions between these elements, which may comprise more effective approaches given the multifaceted protumorigenic mechanisms that drive GBM.

Investigative needle core biopsies for multi-omics in Glioblastoma

Glioblastoma (GBM) is a primary brain cancer with an abysmal prognosis and few effective therapies. The ability to investigate the tumor microenvironment before and during treatment would greatly enhance both understanding of disease response and progression, as well as the delivery and impact of therapeutics. Stereotactic biopsies are a routine surgical procedure performed primarily for diagnostic histopathologic purposes. The role of investigative biopsies - tissue sampling for the purpose of understanding tumor microenvironmental responses to treatment using integrated multi-modal molecular analyses ('Multi-omics") has yet to be defined. Secondly, it is unknown whether comparatively small tissue samples from brain biopsies can yield sufficient information with such methods. Here we adapt stereotactic needle core biopsy tissue in two separate patients. In the first patient with recurrent GBM we performed highly resolved multi-omics analysis methods including single cell RNA sequencing, spatial-transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics from biopsy tissue that was obtained from a single procedure. In a second patient we analyzed multi-regional core biopsies to decipher spatial and genomic variance. We also investigated the utility of stereotactic biopsies as a method for generating patient derived xenograft models in a separate patient cohort. Dataset integration across modalities showed good correspondence between spatial modalities, highlighted immune cell associated metabolic pathways and revealed poor correlation between RNA expression and the tumor MHC Class I immunopeptidome. In conclusion, stereotactic needle biopsy cores are of sufficient quality to generate multi-omics data, provide data rich insight into a patient's disease process and tumor immune microenvironment and can be of value in evaluating treatment responses.

One sentence summary

Integrative multi-omics analysis of stereotactic needle core biopsies in glioblastoma.

Microglia in pediatric brain tumors: The missing link to successful immunotherapy

Brain tumors are the leading cause of cancer-related mortality in children. Despite the development of immunotherapeutic strategies for adult brain tumors, progress in pediatric neuro-oncology has been hindered by the complex and poorly understood nature of the brain's immune system during early development, a phase that is critical for the onset of many pediatric brain tumors. A defining characteristic of these tumors is the abundance of microglia, the resident immune cells of the central nervous system. In this review, we explore the concept of microglial diversity across brain regions and throughout development and discuss how their maturation stage may contribute to tumor growth in children. We also summarize the current knowledge on the roles of microglia in common pediatric brain tumor entities and provide examples of myeloid-based immunotherapeutic strategies. Our review underscores the importance of microglial plasticity in pediatric brain tumors and its significance for developing effective immunotherapeutic strategies.

Drug resistance in glioblastoma: from chemo- to immunotherapy

As the most common and aggressive type of primary brain tumor in adults, glioblastoma is estimated to end over 10,000 lives each year in the United States alone. Stand treatment for glioblastoma, including surgery followed by radiotherapy and chemotherapy (i.e., Temozolomide), has been largely unchanged since early 2000. Cancer immunotherapy has significantly shifted the paradigm of cancer management in the past decade with various degrees of success in treating many hematopoietic cancers and some solid tumors, such as melanoma and non-small cell lung cancer (NSCLC). However, little progress has been made in the field of neuro-oncology, especially in the application of immunotherapy to glioblastoma treatment. In this review, we attempted to summarize the common drug resistance mechanisms in glioblastoma from Temozolomide to immunotherapy. Our intent is not to repeat the well-known difficulty in the area of neuro-oncology, such as the blood-brain barrier, but to provide some fresh insights into the molecular mechanisms responsible for resistance by summarizing some of the most recent literature. Through this review, we also hope to share some new ideas for improving the immunotherapy outcome of glioblastoma treatment.

Dysregulation of inflammasome activation in glioma

Gliomas are the most common brain tumors characterized by complicated heterogeneity. The genetic, molecular, and histological pathology of gliomas is characterized by high neuro-inflammation. The inflammatory microenvironment in the central nervous system (CNS) has been closely linked with inflammasomes that control the inflammatory response and coordinate innate host defenses. Dysregulation of the inflammasome causes an abnormal inflammatory response, leading to carcinogenesis in glioma. Because of the clinical importance of the various physiological properties of the inflammasome in glioma, the inflammasome has been suggested as a promising treatment target for glioma management. Here, we summarize the current knowledge on the contribution of the inflammasomes in glioma and therapeutic insights. Video Abstract.

rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation

Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry. Recent studies have proposed the use of MALDI in-source fragmentation to infer structural information and aid identification. Here we present rMSIfragment, an open-source R package that exploits known adducts and fragmentation pathways to confidently annotate lipids in MALDI-MSI. The annotations are ranked using a novel score that demonstrates an area under the curve of 0.7 in ROC analyses using HPLC-MS and Target-Decoy validations. rMSIfragment applies to multiple MALDI-MSI sample types and experimental setups. Finally, we demonstrate that overlooking in-source fragments increases the number of incorrect annotations. Annotation workflows should consider in-source fragmentation tools such as rMSIfragment to increase annotation confidence and reduce the number of false positives.

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach

Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.

EGFR alterations in glioblastoma play a role in antitumor immunity regulation

The epidermal growth factor receptor (EGFR) is the most frequently altered gene in glioblastoma (GBM), which plays an important role in tumor development and anti-tumor immune response. While current molecular targeted therapies against the EGFR signaling pathway and its downstream key molecules have not demonstrated favorable clinical outcomes in GBM. Whereas tumor immunotherapies, especially immune checkpoint inhibitors, have shown durable antitumor responses in many cancers. However, the clinical efficacy is limited in patients carrying EGFR alterations, indicating that EGFR signaling may involve tumor immune response. Recent studies reveal that EGFR alterations not only promote GBM cell proliferation but also influence immune components in the tumor microenvironment (TME), leading to the recruitment of immunosuppressive cells (e.g., M2-like TAMs, MDSCs, and Tregs), and inhibition of T and NK cell activation. Moreover, EGFR alterations upregulate the expression of immunosuppressive molecules or cytokines (such as PD-L1, CD73, TGF-β). This review explores the role of EGFR alterations in establishing an immunosuppressive TME and hopes to provide a theoretical basis for combining targeted EGFR inhibitors with immunotherapy for GBM.

Chemical QuantArray: A Quantitative Tool for Mass Spectrometry Imaging

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is a powerful analytical technique that provides spatially preserved detection and quantification of analytes in tissue specimens. However, clinical translation still requires improved throughput, precision, and accuracy. To accomplish this, we created "Chemical QuantArray", a gelatin tissue microarray (TMA) mold filled with serial dilutions of isotopically labeled endogenous metabolite standards. The mold is then cryo-sectioned onto a tissue homogenate to produce calibration curves. To improve precision and accuracy, we automatically remove pixels outside of each TMA well and investigated several intensity normalizations, including the utilization of a second stable isotope internal standard (IS). Chemical QuantArray enables the quantification of several endogenous metabolites over a wide dynamic range and significantly improve over current approaches. The technique reduces the space needed on the MALDI slides for calibration standards by approximately 80%. Furthermore, removal of empty pixels and normalization to an internal standard or matrix peak provided precision (<20% RSD) and accuracy (<20% DEV). Finally, we demonstrate the applicability of Chemical QuantArray by quantifying multiple purine metabolites in 14 clinical tumor specimens using a single MALDI slide. Chemical QuantArray improves the analytical characteristics and practical feasibility of MALDI-MSI metabolite quantification in clinical and translational applications.

Emerging roles of nucleotide metabolism in cancer

Nucleotides are substrates for multiple anabolic pathways, most notably DNA and RNA synthesis. Since nucleotide synthesis inhibitors began to be used for cancer therapy in the 1950s, our understanding of how nucleotides function in tumor cells has evolved, prompting a resurgence of interest in targeting nucleotide metabolism for cancer therapy. In this review, we discuss recent advances that challenge the idea that nucleotides are mere building blocks for the genome and transcriptome and highlight ways that these metabolites support oncogenic signaling, stress resistance, and energy homeostasis in tumor cells. These findings point to a rich network of processes sustained by aberrant nucleotide metabolism in cancer and reveal new therapeutic opportunities.

Metabolic partitioning in the brain and its hijacking by glioblastoma

The different cell types in the brain have highly specialized roles with unique metabolic requirements. Normal brain function requires the coordinated partitioning of metabolic pathways between these cells, such as in the neuron-astrocyte glutamate-glutamine cycle. An emerging theme in glioblastoma (GBM) biology is that malignant cells integrate into or "hijack" brain metabolism, co-opting neurons and glia for the supply of nutrients and recycling of waste products. Moreover, GBM cells communicate via signaling metabolites in the tumor microenvironment to promote tumor growth and induce immune suppression. Recent findings in this field point toward new therapeutic strategies to target the metabolic exchange processes that fuel tumorigenesis and suppress the anticancer immune response in GBM. Here, we provide an overview of the intercellular division of metabolic labor that occurs in both the normal brain and the GBM tumor microenvironment and then discuss the implications of these interactions for GBM therapy.

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.

A review on deep learning applications in highly multiplexed tissue imaging data analysis

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

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.

The promise of metabolic imaging in diffuse midline glioma

Recent insights into histopathological and molecular subgroups of glioma have revolutionized the field of neuro-oncology by refining diagnostic categories. An emblematic example in pediatric neuro-oncology is the newly defined diffuse midline glioma (DMG), H3 K27-altered. DMG represents a rare tumor with a dismal prognosis. The diagnosis of DMG is largely based on clinical presentation and characteristic features on conventional magnetic resonance imaging (MRI), with biopsy limited by its delicate neuroanatomic location. Standard MRI remains limited in its ability to characterize tumor biology. Advanced MRI and positron emission tomography (PET) imaging offer additional value as they enable non-invasive evaluation of molecular and metabolic features of brain tumors. These techniques have been widely used for tumor detection, metabolic characterization and treatment response monitoring of brain tumors. However, their role in the realm of pediatric DMG is nascent. By summarizing DMG metabolic pathways in conjunction with their imaging surrogates, we aim to elucidate the untapped potential of such imaging techniques in this devastating disease.

CD73 in glioblastoma: Where are we now and what are the future directions?

Glioblastoma (GB) is the most aggressive type of brain tumor with heterogeneity, strong invasive ability, and high resistance to therapy due to immunosuppressive mechanisms. CD73 is an overexpressed enzyme in GB acts via two main mechanisms:(1) CD73 acts as an adhesion protein independent of the enzymatic activity or (2) via the catalyses of AMP to adenosine (ADO) generating a strong modulatory molecule that induces alterations in the tumor cells and in the tumor microenvironment cells (TME). Taken together, CD73 is receiving attention during the last years and studies demonstrated its dual potential benefit as a target to GB therapy. Here, we review the roles of CD73 and P1 receptors (ADO receptors) in GB, the impact of CD73 in the immune interactions between tumor and other immune cells, the proposed therapeutic strategies based on CD73 regulation, and discuss the gap in knowledge and further directions to bring this approach from preclinical to clinical use.

Radio-pathomic approaches in pediatric neuro-oncology: Opportunities and challenges

With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models.

SLC11A1 as a stratification indicator for immunotherapy or chemotherapy in patients with glioma

Background

Glioma is a fatal tumor originating from the brain, which accounts for most intracranial malignancies. Currently, Immunotherapy has turned into a novel and promising treatment in glioma patients. however, there are still few effective biomarkers to mirror the reaction to immunotherapy in patients with glioma. Therefore, we intended to elucidate the evaluable efficacy of SLC11A1 in glioma patients.

Methods

In this study, samples from Shanghai General Hospital and data from TCGA, GEO, CGGA datasets were used to investigate and validate the relationship between SLC11A1 and the progression of glioma. We evaluated the predictive value of SLC11A1 on the prognosis of glioma with cox regression analysis. Then the relationship between immune infiltration and SLC11A1 was also analyzed. Ultimately, we performed the prediction on the immunotherapeutic response and therapeutic drugs according to the expression of SLC11A1.

Results

Expression of SLC11A1 increased with progression and predicted unfavorable prognosis for glioma patients. The hazard ratio for SLC11A1 expression was 2.33 with 95% CI (1.92-2.58) (P < 0.001) in cox analysis. And based on expression, we found SLC11A1 stratified glioma patients into subgroups with different immune activation statuses. Moreover, we observed that patients with higher SLC11A1 levels companied with better immunotherapeutic response, while those with lower SLC11A1 levels may respond better to temozolomide.

Conclusion

This study provided evidence that SLC11A1 was a novel prognostic marker and immunotherapy response indicator for gliomas. In some cases, SLC11A1 could be an effective marker for identifying patients who might benefit from immunotherapy or chemotherapy.

Exploring glioblastoma stem cell heterogeneity: Immune microenvironment modulation and therapeutic opportunities

Despite its growing use in cancer treatment, immunotherapy has been virtually ineffective in clinical trials for gliomas. The inherently cold tumor immune microenvironment (TIME) in gliomas, characterized by a high ratio of pro-tumor to anti-tumor immune cell infiltrates, acts as a seemingly insurmountable barrier to immunotherapy. Glioma stem cells (GSCs) within these tumors are key contributors to this cold TIME, often functioning indirectly through activation and recruitment of pro-tumor immune cell types. Furthermore, drivers of GSC plasticity and heterogeneity (e.g., reprogramming transcription factors, epigenetic modifications) are associated with induction of immunosuppressive cell states. Recent studies have identified GSC-intrinsic mechanisms, including functional mimicry of immune suppressive cell types, as key determinants of anti-tumor immune escape. In this review, we cover recent advancements in our understanding of GSC-intrinsic mechanisms that modulate GSC-TIME interactions and discuss cutting-edge techniques and bioinformatics platforms available to study immune modulation at high cellular resolution with exploration of both malignant (i.e., GSC) and non-malignant (i.e., immune) cell fractions. Finally, we provide insight into the therapeutic opportunities for targeting immunomodulatory GSC-intrinsic mechanisms to potentiate immunotherapy response in gliomas.