Resolution of tissue signatures of therapy response in patients with recurrent GBM treated with neoadjuvant anti-PD1

Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features

Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors, and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. As an alternative way of investigating characteristics of brain tumor grades, we implemented a protocol for learning, discovering, and quantifying tumor microenvironment elements on our glioma dataset. Using only single-stained biopsies we derived characteristic differentiating tumor microenvironment phenotypic neighborhoods. The study was complicated by the small size of the available human leukocyte antigen stained on glioma tissue microarray dataset - 206 images of 5 classes - as well as imbalanced data distribution. This challenge was addressed by image augmentation for underrepresented classes. In practice, we considered two scenarios, a whole slide supervised learning classification, and an unsupervised cell-to-cell analysis looking for patterns of the microenvironment. In the supervised learning investigation, we evaluated 6 distinct model architectures. Experiments revealed that a DenseNet121 architecture surpasses the baseline's accuracy by a significant margin of 9% for the test set, achieving a score of 69%, increasing accuracy in discerning challenging WHO grade 2 and 3 cases. All experiments have been carried out in a cross-validation manner. The tumor microenvironment analysis suggested an important role for myeloid cells and their accumulation in the context of characterizing glioma grades. Those promising approaches can be used as an additional diagnostic tool to improve assessment during intraoperative examination or subtyping tissues for treatment selection, potentially easing the workflow of pathologists and oncologists.

WNT/β-catenin regulatory roles on PD-(L)1 and immunotherapy responses

Dysregulation of WNT/β-catenin is a hallmark of many cancer types and a key mediator of metastasis in solid tumors. Overactive β-catenin signaling hampers dendritic cell (DC) recruitment, promotes CD8+ T cell exclusion and increases the population of regulatory T cells (Tregs). The activity of WNT/β-catenin also induces the expression of programmed death-ligand 1 (PD-L1) on tumor cells and promotes programmed death-1 (PD-1) upregulation. Increased activity of WNT/β-catenin signaling after anti-PD-1 therapy is indicative of a possible implication of this signaling in bypassing immune checkpoint inhibitor (ICI) therapy. This review is aimed at giving a comprehensive overview of the WNT/β-catenin regulatory roles on PD-1/PD-L1 axis in tumor immune ecosystem, discussing about key mechanistic events contributed to the WNT/β-catenin-mediated bypass of ICI therapy, and representing inhibitors of this signaling as promising combinatory regimen to go with anti-PD-(L)1 in cancer immunotherapy. Ideas presented in this review imply the synergistic efficacy of such combination therapy in rendering durable anti-tumor immunity.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

Organ-specific immunity: A tissue analysis framework for investigating local immune responses to SARS-CoV-2

Local immune activation at mucosal surfaces, mediated by mucosal lymphoid tissues, is vital for effective immune responses against pathogens. While pathogens like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread to multiple organs, patients with coronavirus disease 2019 (COVID-19) primarily experience inflammation and damage in their lungs. To investigate this apparent organ-specific immune response, we develop an analytical framework that recognizes the significance of mucosal lymphoid tissues. This framework combines histology, immunofluorescence, spatial transcript profiling, and mathematical modeling to identify cellular and gene expression differences between the lymphoid tissues of the lung and the gut and predict the determinants of those differences. Our findings indicate that mucosal lymphoid tissues are pivotal in organ-specific immune response to SARS-CoV-2, mediating local inflammation and tissue damage and contributing to immune dysfunction. The framework developed here has potential utility in the study of long COVID and may streamline biomarker discovery and treatment design for diseases with differential pathologies at the organ level.

FCGR2B as a prognostic and immune microenvironmental marker for gliomas based on transcriptomic analysis

To explore the expression and prognosis of Fc fragment of IgG low affinity IIb receptor (FCGR2B) in glioma and its relationship with immune microenvironment, so as to provide potential molecular targets for the treatment of glioma. We analyzed the gene expression of FCGR2B using the Cancer Genome Atlas database, Chinese Glioma Genome Atlas, Gene Expression Omnibus database and other glioma related databases. Moreover, we generated survival receiver operating characteristic curve, carried out univariate and multivariate Cox analysis and nomograph construction, and analyzed the relationship between FCGR2B and prognosis. According to the median of FCGR2B gene expression value, the differential expression analysis was carried out by high and low grouping method, and the gene ontology, Kyoto encyclopedia of genes and genomes, and gene set enrichment analysis enrichment analysis were carried out to explore the possible mechanism. Then, the correlation between immune score of glioma and prognosis, World Health Organization grade and FCGR2B expression was analyzed. Finally, the correlation between FCGR2B expression and the proportion of tumor infiltrating immune cells, immune checkpoints, tumor mutation load and immune function was analyzed. The expression of FCGR2B in gliomas was higher than that in normal tissues and was associated with poor prognosis. Independent prognostic analysis showed that FCGR2B was an independent prognostic factor for glioma. The analysis of gene ontology and gene set enrichment analysis showed that FCGR2B was closely related to immune-related functions. The analysis of immune scores and prognosis, World Health Organization grade and FCGR2B expression in gliomas indicated that patients with high immune scores had significantly poorer overall survival and higher tumor pathological grade. In addition, immune scores were significantly positively correlated with the expression of FCGR2B. The analysis of tumor infiltrating immune cells suggested that the expression level of FCGR2B affected the immune activity of TME. In addition, the expression of FCGR2B was positively correlated with almost all immune checkpoint molecules including CD28, CD44, TNFSF14, PDCD1LG2, LAIR1, and CD48 and was significantly positively correlated with tumor mutation load. All immunobiological functions of the high expression group of FCGR2B were significantly inhibited. FCGR2B may play an important role in the occurrence, development and invasion of tumor by influencing the tumor microenvironment of immunosuppression. FCGR2B may be an important target for the treatment of glioma.

Biomaterial-based in vitro 3D modeling of glioblastoma multiforme

Adult-onset brain cancers, such as glioblastomas, are particularly lethal. People with glioblastoma multiforme (GBM) do not anticipate living for more than 15 months if there is no cure. The results of conventional treatments over the past 20 years have been underwhelming. Tumor aggressiveness, location, and lack of systemic therapies that can penetrate the blood-brain barrier are all contributing factors. For GBM treatments that appear promising in preclinical studies, there is a considerable rate of failure in phase I and II clinical trials. Unfortunately, access becomes impossible due to the intricate architecture of tumors. In vitro, bioengineered cancer models are currently being used by researchers to study disease development, test novel therapies, and advance specialized medications. Many different techniques for creating in vitro systems have arisen over the past few decades due to developments in cellular and tissue engineering. Later-stage research may yield better results if in vitro models that resemble brain tissue and the blood-brain barrier are used. With the use of 3D preclinical models made available by biomaterials, researchers have discovered that it is possible to overcome these limitations. Innovative in vitro models for the treatment of GBM are possible using biomaterials and novel drug carriers. This review discusses the benefits and drawbacks of 3D in vitro glioblastoma modeling systems.

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.

Artificial intelligence for prediction of response to cancer immunotherapy

Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-care systems in multiple areas such as diagnostic confirmation, risk stratification, analysis, prognosis prediction, treatment surveillance, and virtual health support, which has considerable potential to revolutionize and reshape medicine. In terms of immunotherapy, AI has been applied to unlock underlying immune signatures to associate with responses to immunotherapy indirectly as well as predict responses to immunotherapy responses directly. The AI-based analysis of high-throughput sequences and medical images can provide useful information for management of cancer immunotherapy considering the excellent abilities in selecting appropriate subjects, improving therapeutic regimens, and predicting individualized prognosis. In present review, we aim to evaluate a broad framework about AI-based computational approaches for prediction of response to cancer immunotherapy on both indirect and direct manners. Furthermore, we summarize our perspectives about challenges and opportunities of further AI applications on cancer immunotherapy relating to clinical practicability.

Targeting FGL2 in glioma immunosuppression and malignant progression

Glioblastoma (GBM) is the most malignant type of glioma with the worst prognosis. Traditional therapies (surgery combined with radiotherapy and chemotherapy) have limited therapeutic effects. As a novel therapy emerging in recent years, immunotherapy is increasingly used in glioblastoma (GBM), so we expect to discover more effective immune targets. FGL2, a member of the thrombospondin family, plays an essential role in regulating the activity of immune cells and tumor cells in GBM. Elucidating the role of FGL2 in GBM can help improve immunotherapy efficacy and design treatment protocols. This review discusses the immunosuppressive role of FGL2 in the GBM tumor microenvironment and its ability to promote malignant tumor progression while considering FGL2-targeted therapeutic strategies. Also, we summarize the molecular mechanisms of FGL2 expression on various immune cell types and discuss the possibility of FGL2 and its related mechanisms as new GBM immunotherapy.

Mechanism and therapeutic potential of tumor-immune symbiosis in glioblastoma

Glioblastoma (GBM) is the most aggressive and lethal form of brain tumor in human adults. Myeloid-lineage cells, including macrophages, microglia, myeloid-derived suppressor cells (MDSCs), and neutrophils, are the most frequent types of cell in the GBM tumor microenvironment (TME) that contribute to tumor progression. Emerging experimental evidence indicates that symbiotic interactions between cancer cells and myeloid cells are critical for tumor growth and immunotherapy resistance in GBM. In this review, we discuss the molecular mechanisms whereby cancer cells shape a myeloid cell-mediated immunosuppressive TME and, reciprocally, how such myeloid cells affect tumor progression and immunotherapy efficiency in GBM. Moreover, we highlight tumor-T cell symbiosis and summarize immunotherapeutic strategies intercepting this co-dependency in GBM.

Construction and Validation of a 6-Ferroptosis Related Gene Signature for Prognosis and Immune Landscape Prediction in Melanoma

Ferroptosis is a newly discovered form of non-apoptotic cell death that relies on iron-mediated oxidative damage, playing a crucial role in the progression and therapy resistance of melanoma. Hence, the potential value of ferroptosis-related genes (FRGs) as a prognostic model and therapeutic target in melanoma requires further investigation. In this study, the relationship between FRGs and melanoma was revealed by analyzing the mRNA expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO). A 6-FRGs signature was constructed by Univariate, multivariate, and lasso Cox regression analyses in the TCGA cohort. The GEO database was used to validate the efficacy of the signature. The protein and mRNA expression level of the signature genes were examined in real-world melanoma tissues via immunohistochemical and quantificational real-time polymerase chain reaction (qRT-PCR). Functional enrichment analysis and immune-related analysis were conducted to identify the potential biological functions and pathways of the signature. Ten putative small molecule drugs were predicted by Connectivity Map (CMAP). As a result, a 6-FRGs signature was constructed to stratify melanoma patients into two risk groups. Compared with the low-risk group, patients in the high-risk group had a worse prognosis and a lower ImmuneScore. Immune-related pathways were enriched in the low-risk group. Immune Function and immune cell infiltration of the low-risk group were significantly higher than that of the high-risk group. The differential expression of these six FRGs in melanoma and adjacent normal tissues was confirmed. Moreover, higher expression of immune checkpoint molecules and a greater sensitivity to immunotherapy were observed in the low-risk group. Some small molecular drugs in the CMAP database hold the potential to treat melanoma. Overall, we identified a novel FRGs signature for prognostic prediction in melanoma. Based on the signature-related immune infiltration landscape found in our study, targeting the FRGs might be a therapeutic alternative for melanoma.

The future of cancer immunotherapy for brain tumors: a collaborative workshop

Harnessing the effector mechanisms of the immune system to combat brain tumors with antigen specificity and memory has been in research and clinical testing for many years. Government grant mechanisms and non-profit organizations have supported many innovative projects and trials while biotech companies have invested in the development of needed tools, assays and novel clinical approaches. The National Brain Tumor Society and the Parker Institute for Cancer Immunotherapy partnered to host a workshop to share recent data, ideas and identify both hurdles and new opportunities for harnessing immunotherapy against pediatric and adult brain tumors. Adoptively transferred cell therapies have recently shown promising early clinical results. Local cell delivery to the brain, new antigen targets and innovative engineering approaches are poised for testing in a new generation of clinical trials. Although several such advances have been made, several obstacles remain for the successful application of immunotherapies for brain tumors, including the need for more representative animal models that can better foreshadow human trial outcomes. Tumor and tumor microenvironment biopsies with multiomic analysis are critical to understand mechanisms of response and patient stratification, yet brain tumors are especially challenging for such biopsy collection. These workshop proceedings and commentary shed light on the status of immunotherapy in pediatric and adult brain tumor patients, including current research as well as opportunities for improving future efforts to bring immunotherapy to the forefront in the management of brain tumors.

Central nervous system immune interactome is function of cancer lineage, tumor microenvironment and STAT3 expression

BACKGROUND

Immune cell profiling of primary and metastatic CNS tumors has been focused on the tumor, not the tumor microenvironment (TME), or has been analyzed via biopsies.

METHODS

En bloc resections of gliomas (n = 10) and lung metastases (n = 10) were analyzed via tissue segmentation and high-dimension Opal 7-color multiplex imaging. Single-cell RNA analyses were used to infer immune cell functionality.

RESULTS

Within gliomas, T cells were localized in the infiltrating edge and perivascular space of tumors, while residing mostly in the stroma of metastatic tumors. CD163⁺ macrophages were evident throughout the TME of metastatic tumors, whereas in gliomas, CD68⁺, CD11c⁺CD68⁺, and CD11c⁺CD68⁺CD163⁺ cell subtypes were commonly observed. In lung metastases, T cells interacted with CD163⁺ macrophages as dyads and clusters at the brain-tumor interface and within the tumor itself and as clusters within the necrotic core. In contrast, gliomas typically lacked dyad and cluster interactions, except for T cell CD68⁺ cell dyads within the tumor. Analysis of transcriptomic data in glioblastomas revealed that innate immune cells expressed both proinflammatory and immunosuppressive gene signatures.

CONCLUSION

Our results show that immunosuppressive macrophages are abundant within the TME and that the immune cell interactome between cancer lineages is distinct. Further, these data provide information for evaluating the role of different immune cell populations in brain tumor growth and therapeutic responses.

FUNDING

This study was supported by the NIH (NS120547), a Developmental research project award (P50CA221747), ReMission Alliance, institutional funding from Northwestern University and the Lurie Comprehensive Cancer Center, and gifts from the Mosky family and Perry McKay. Performed in the Flow Cytometry & Cellular Imaging Core Facility at MD Anderson Cancer Center, this study received support in part from the NIH (CA016672) and the National Cancer Institute (NCI) Research Specialist award 1 (R50 CA243707). Additional support was provided by CCSG Bioinformatics Shared Resource 5 (P30 CA046592), a gift from Agilent Technologies, a Research Scholar Grant from the American Cancer Society (RSG-16-005-01), a Precision Health Investigator Award from University of Michigan (U-M) Precision Health, the NCI (R37-CA214955), startup institutional research funds from U-M, and a Biomedical Informatics & Data Science Training Grant (T32GM141746).

The Advances in Glioblastoma On-a-Chip for Therapy Approaches

Simple Summary

This systematic review showed different therapeutic approaches to glioblastoma on-a-chip with varying levels of complexity, answering, from the simplest question to the most sophisticated questions, in a biological system integrated in an efficient way. With advances in manufacturing protocols, soft lithography in PDMS material was the most used in the studies, applying different strategy geometrics in device construction. The microenvironment showed the relevant elaborations in co-culture between mainly human tumor cells and support cells involved in the collagen type I matrix; remaining an adequate way to assess the therapeutic approach. The most complex devices showed efficient intersection between different systems, allowing in vitro studies with major human genetic similarity, reproducibility, and low cost, on a highly customizable platform.

Abstract

This systematic review aimed to verify the use of microfluidic devices in the process of implementing and evaluating the effectiveness of therapeutic approaches in glioblastoma on-a-chip, providing a broad view of advances to date in the use of this technology and their perspectives. We searched studies with the variations of the keywords “Glioblastoma”, “microfluidic devices”, “organ-on-a-chip” and “therapy” of the last ten years in PubMed and Scopus databases. Of 446 articles identified, only 22 articles were selected for analysis according to the inclusion and exclusion criteria. The microfluidic devices were mainly produced by soft lithography technology, using the PDMS material (72%). In the microenvironment, the main extracellular matrix used was collagen type I. Most studies used U87-MG glioblastoma cells from humans and 31.8% were co-cultivated with HUVEC, hCMEC/D3, and astrocytes. Chemotherapy was the majority of therapeutic approaches, assessing mainly the cellular viability and proliferation. Furthermore, some alternative therapies were reported in a few studies (22.6%). This study identified a diversity of glioblastoma on-a-chip to assess therapeutic approaches, often using intermediate levels of complexity. The most advanced level implemented the intersection between different biological systems (liver–brain or intestine–liver–brain), BBB model, allowing in vitro studies with greater human genetic similarity, reproducibility, and low cost, in a highly customizable platform.

The Eclectic Nature of Glioma-Infiltrating Macrophages and Microglia

Glioblastomas (GBMs) are complex ecosystems composed of highly multifaceted tumor and myeloid cells capable of responding to different environmental pressures, including therapies. Recent studies have uncovered the diverse phenotypical identities of brain-populating myeloid cells. Differences in the immune proportions and phenotypes within tumors seem to be dictated by molecular features of glioma cells. Furthermore, increasing evidence underscores the significance of interactions between myeloid cells and glioma cells that allow them to evolve in a synergistic fashion to sustain tumor growth. In this review, we revisit the current understanding of glioma-infiltrating myeloid cells and their dialogue with tumor cells in consideration of their increasing recognition in response and resistance to immunotherapies as well as the immune impact of the current chemoradiotherapy used to treat gliomas.

Deciphering the Labyrinthine System of the Immune Microenvironment in Recurrent Glioblastoma: Recent Original Advances and Lessons from Clinical Immunotherapeutic Approaches

The interpretation of the presence and function of immune infiltration in glioblastoma (GBM) is still debated. Over the years, GBM has been considered a cold tumor that is less infiltrated by effector cells and characterized by a high proportion of immunosuppressive innate immune cells, including GBM-associated microglia/macrophages (GAMs). In this context, the failure of checkpoint inhibitors, particularly in recurrent GBM (rGBM), caused us to look beyond the clinical results and consider the point of view of immune cells. The tumor microenvironment in rGBM can be particularly hostile, even when exposed to standard immunomodulatory therapies, and tumor-infiltrating lymphocytes (TILs), when present, are either dysfunctional or terminally exhausted. However, after checkpoint blockade therapy, it was possible to observe specific recruitment of adaptive immune cells and an efficient systemic immune response. In this review article, we attempt to address current knowledge regarding the tumor and immune microenvironment in rGBM. Furthermore, immunosuppression induced by GAMs and TIL dysfunction was revisited to account for genetic defects that can determine resistance to therapies and manipulate the immune microenvironment upon recurrence. Accordingly, we reevaluated the microenvironment of some of our rGBM patients treated with dendritic cell immunotherapy, with the goal of identifying predictive immune indicators of better treatment response.