Glioma: bridging the tumor microenvironment, patient immune profiles and novel personalized immunotherapy

Abstract

Glioma is the most common primary brain tumor, characterized by a consistently high patient mortality rate and a dismal prognosis affecting both survival and quality of life. Substantial evidence underscores the vital role of the immune system in eradicating tumors effectively and preventing metastasis, underscoring the importance of cancer immunotherapy which could potentially address the challenges in glioma therapy. Although glioma immunotherapies have shown promise in preclinical and early-phase clinical trials, they face specific limitations and challenges that have hindered their success in further phase III trials. Resistance to therapy has been a major challenge across many experimental approaches, and as of now, no immunotherapies have been approved. In addition, there are several other limitations facing glioma immunotherapy in clinical trials, such as high intra- and inter-tumoral heterogeneity, an inherently immunosuppressive microenvironment, the unique tissue-specific interactions between the central nervous system and the peripheral immune system, the existence of the blood-brain barrier, which is a physical barrier to drug delivery, and the immunosuppressive effects of standard therapy. Therefore, in this review, we delve into several challenges that need to be addressed to achieve boosted immunotherapy against gliomas. First, we discuss the hurdles posed by the glioma microenvironment, particularly its primary cellular inhabitants, in particular tumor-associated microglia and macrophages (TAMs), and myeloid cells, which represent a significant barrier to effective immunotherapy. Here we emphasize the impact of inducing immunogenic cell death (ICD) on the migration of Th17 cells into the tumor microenvironment, converting it into an immunologically “hot” environment and enhancing the effectiveness of ongoing immunotherapy. Next, we address the challenge associated with the accurate identification and characterization of the primary immune profiles of gliomas, and their implications for patient prognosis, which can facilitate the selection of personalized treatment regimens and predict the patient’s response to immunotherapy. Finally, we explore a prospective approach to developing highly personalized vaccination strategies against gliomas, based on the search for patient-specific neoantigens. All the pertinent challenges discussed in this review will serve as a compass for future developments in immunotherapeutic strategies against gliomas, paving the way for upcoming preclinical and clinical research endeavors.

1 Introduction

Glioma, the most common and deadliest malignant primary brain tumor in adults, poses a formidable challenge. However, despite the constant updating and improvement of neurosurgical techniques and sophisticated immunotherapeutic regimens (e.g., chimeric antigen receptor T-cell and NK-cell therapies, immune checkpoint inhibitors, gene-mediated cytotoxic immunotherapy and oncolytic viruses), the survival rate of patients with glioma remains very low and prognosis very poor in terms of survival and quality of life (1–5).

Gliomas usually originate from glial cells and affect both the brain and spinal cord. The 2021 WHO Classification of Tumors of the CNS, 5^th edition (WHO CNS 5) recognizes the following four families of gliomas: 1) adult-type diffuse gliomas; 2) pediatric-type diffuse low-grade gliomas; 3) pediatric-type diffuse high-grade gliomas; and 4) circumscribed astrocytic gliomas (6, 7). The current treatment of glioma is ineffective, prolonging the patient’s life by only 2.5 years. Therefore, there is an active search for new therapeutic strategies that could give hope to thousands of patients. According to the aforementioned WHO classification of CNS tumors (WHO CNS 5), grading malignancy and choosing a therapeutic strategy emphasize the molecular markers (6, 8), sometimes even more than the histological features (9). This approach can yield powerful prognostic information (10, 11). In addition, several important molecular alterations for characterizing the genetic profile of gliomas have been identified such as isocitrate dehydrogenase 1 and 2 (IDH1, IDH2), telomerase reverse transcriptase (TERT, including promoter region), O-6-methylguanine-DNA methyltransferase (MGMT) and alpha-thalassemia/mental retardation, X-linked, B-Raf proto-oncogene serine/threonine kinase, tumor protein p53, epidermal growth factor receptor amplification or mutation, cyclin-dependent kinase inhibitor 2A or 2B, codeletion of chromosome arms 1p and 19q, combined gain of chromosome 7 and loss of chromosome 10. The most notable molecular subtypes of glioma, according to the WHO Classification of Tumors of the CNS, 4th edition, are IDH mutations and the 1p/19q deletion (12). Moreover, the molecular features have been found to be associated with distinct immune landscapes and prognosis, suggesting a link between molecular and immune subtypes in gliomas (10, 13–18).

Modern therapeutic approaches to the treatment of gliomas rely on clinical presentation, tumor grade, tumor size, and tumor location. However, surgery, radiotherapy (RT) and systemic therapy, including chemotherapy and targeted therapy, remain the main approaches. Combinations of these methods are usually applied to contain the progression of the disease (Table 1).

One of the initial steps in the treatment of gliomas is surgical resection, which removes the whole tumor and provides a biopsy for histological analysis and tumor genotyping (11, 56). However, there are limitations to the complete removal of gliomas by surgery due to their invasiveness in the surrounding tissues, and the peculiarities of their microenvironment, which lead to frequent relapses. To increase resection efficiency and control damage to healthy tissues, additional methods are used, such as fluorescence-guided resection with 5-aminolevulinic acid (5-ALA) (57–60), and motor and speech mapping through intraoperative cortical electrodes (61–63), along with intraoperative magnetic resonance imaging (MRI) (64, 65). In addition, to avoid subsequent malignant progression, surgical resection is often followed by RT alone or in combination with chemotherapy, depending on the tumor type (66–68). The choice of RT regimen (time, dosage, schedule) is based on the diagnosis and prognostic factors, including age, degree of resection, and Karnofsky score (69, 70). The Stupp protocol, named after Swiss oncologist Roger Stupp, has become the standard of care for the treatment of glioblastoma since its publication in 2005 and has led to significant prolongation of survival (Box 1). Modern methods of focused radiation therapy, such as intensity-modulated or image-guided radiation therapy and stereotactic radiation therapy, can improve the targeted delivery of RT for better protection of surrounding tissues (71, 72).

The use of chemotherapy in patients with glioma plays an important role in preventing postoperative recurrence. Modern glioma therapy practices use several different chemotherapeutic agents in combination with other treatment modalities to improve therapy efficacy (73). Although many drugs have been developed, the Food and Drug Administration (FDA) has approved only a few for the treatment of glioma, of which alkylating agents such as temozolomide (TMZ) are widely used. TMZ can methylate DNA, which most often occurs at the N-7 or O-6 position of guanine residues, inducing cell cycle arrest and apoptosis of cancer cells (73–77). MGMT promoter methylation is of the greatest clinical importance for predicting responses to TMZ (78, 79). However, TMZ toxicity and drug resistance limit its effectiveness, highlighting the importance of exploring and developing new treatment approaches (80–82). Another treatment option is chemotherapy with “second-line” nitrosourea-based drugs, including nimustine, carmustine, lomustine and ranimustine (83). Unfortunately, their action mechanisms, effectiveness, and side effects are comparable to those of TMZ and other alkylating agents, but they can be used to overcome TMZ resistance (77, 84, 85).

Another chemotherapeutic approach for glioma is platinum-based cancer therapy. Cisplatin, carboplatin and oxaliplatin have been widely used to treat brain tumors and other cancers (86). These drugs induce various cellular responses, such as cell cycle arrest, transcription inhibition, DNA repair, apoptosis (87, 88) and ICD (89, 90). The main limitations of these drugs are their instability, impossibility of oral administration, and poor permeability through the blood-brain barrier (BBB) (73). In addition, doxorubicin, vincristine and topotecan are used mainly in combination regimens (91–93).

Effective chemotherapeutic drugs with reduced side effects could be produced by using alternative drug delivery systems such as liposome encapsulation, peptide-protein conjugates, and polymeric micelles and nanoparticles. However, little success in delivery and side effects reduction have been achieved (73). Thus, there are currently not so many effective treatment options for glioma therapy, and the search for new approaches is a pressing issue.

New therapies based on molecular profiling, new small molecules, and novel immunotherapeutic approaches may improve the survival and quality of life of patients with gliomas. Now, there are high hopes for immunotherapy, as immunotherapeutic strategies have been repeatedly proven to be effective and safe and are likely to be increasingly used in the future. Several immunotherapeutic strategies are currently being applied or adapted in clinical trials for the treatment of gliomas. These strategies include the use of immune checkpoint inhibitors (94–96) (e.g., ClinicalTrials.gov: NCT04267146, NCT03557359, NCT03925246), chimeric antigen receptor (CAR) T cell therapy (97–99) (e.g., ClinicalTrials.gov: NCT06018363, NCT02208362, NCT01454596), oncolytic viruses (100, 101) (e.g., ClinicalTrials.gov: NCT06126744, NCT02062827, NCT00528684), gene-mediated cytotoxic immunotherapy (102) (e.g., ClinicalTrials.gov: NCT00751270, NCT03576612, NCT00589875) and therapeutic anti-tumor vaccination (103, 104) (e.g., ClinicalTrials.gov: NCT05283109, NCT03665545, NCT02507583). While all of these strategies hold promise as potential breakthroughs in glioma treatment, their efficacy and safety must be rigorously evaluated. Well-designed clinical trials are essential to address the challenges posed by the immunosuppressive tumor microenvironment, antigen heterogeneity, and antigen escape.

In this review, we first highlight the challenges presented by the glioma microenvironment and immune profiles of glioma, which pose a significant obstacle for effective immunotherapy, and highlight the impact of Th17 cells on the efficacy of glioma immunotherapy. Next, we delve into the primary immune profiles associated with gliomas and their implications for disease prognosis in patients and discuss a prospect approach to developing the most personalized vaccination strategy against glioma based on patient-specific neoantigens.

2 Bottleneck in glioma immunotherapy: the glioma microenvironment

Glioblastoma multiforme (GBM, WHO grade 4) typically has a relatively low mutation load and immunologically ‘cold’ tumor microenvironment (TME) (105–107) that abrogates T-cell infiltration and activation (Figure 1). This is the current great challenge in immunotherapy. Importantly, the components of TME in GBM resemble those in other tumors, but they also include some unique brain tissue-resident cell types. To complicate matters, the cellular composition of the TME exhibits substantial variability among patients with GBM. More than 30% of infiltrating cells in the TME are robust tumor-associated microglia and macrophages (TAMs) (106, 108, 116). At the same time, glial and myeloid cell populations in the TME of GBM are highly heterogeneous, which can affect the efficacy of (immuno)therapeutic regimens and complicate the prediction of response to (immuno)therapy in individual patients (111) (Figure 1).

Figure 1

It has been reported that primary tumors of patients with GBM, which have increased levels of M2-like TAMs (CD204⁺ cells), were responsible for a more-resistant pro-tumorigenic microenvironment and thus were associated with GBM aggressiveness, treatment resistance, and poor prognosis (110) (Figure 1). It is of interest that another study also demonstrated a significant upregulation of M2 macrophages and their association with poor prognosis in patients with low-grade gliomas (117). However, recent studies have provided a breakthrough in characterizing and understanding the TME and the individual immune components, including the specific interactions between myeloid cells and glioma cells or myeloid cells and tumor-infiltrating lymphocytes. This progress renders the simplistic paradigm of M1/M2 phenotypes of TAMs no longer a viable option. In this regard, a recent study demonstrates substantial cellular and functional heterogeneity of myeloid cells in TME associated not only with different cell distributions in the tumor core and tumor periphery but also with sex-specific alterations in the responses of myeloid cells to gliomas (118). The study reveals significant upregulation of genes of the MHC II complex in microglia and monocytes/macrophages populations in male mice compared to female mice (118). High heterogeneity within the GBM myeloid compartment was uncovered through scRNA-seq and simultaneous epitope and transcriptome measurements in single cells (CITE-seq analysis) of the glioblastoma immune landscape in mouse tumors and in patients with newly diagnosed or recurrent disease (109). The authors revealed that microglia- or monocyte-derived populations of TAMs exhibit additional heterogeneity, including subsets with conserved lipid and hypoxic signatures. The dominance of microglia-derived TAMs was established in newly diagnosed tumors and suggests particular prognostic significance. In contrast, in recurrent tumors, the profile shifts to monocyte-derived TAMs, especially in hypoxic tumor environments (109). Equally interesting is the discovery of similarities between the human and mouse GBM immune compartments, highlighting the value of the GL261 experimental glioma as useful preclinical mouse model (109).

Of interest that scRNAseq and imaging mass cytometry-based single-cell profiling have provided evidence that hypoxic active regions in the tumor triggers cell-cycle arrest, particular an S-phase arrest (119) This contributes to the accumulation of genomic instabilities, facilitating microevolution toward resilience in GBM. Furthermore, both reactive-immune and hypoxia areas revealed a significant enrichment of TAMs and exhausted T cells suggesting a local enhanced immunosuppression (119). It is noteworthy that a recent study has shown the presence of a subset of heme oxygenase 1⁺ myeloid cells in the GBM microenvironment (112). These cells are responsible for releasing IL-10, which leads to dysfunctional T cell transformation via activation of the JAK-STAT pathway, which is a major driving force behind T-cell exhaustion (Figure 1).

Importantly, recent scRNAseq analysis of human glioma specimens (i.e., low-grade gliomas, newly diagnosed GBMs, and recurrent GBMs) has allowed the identification of nine distinct myeloid cell subtypes with unique gene expression patterns (MC1–MC9). Among these, five subtypes (MC2–MC5, and MC7) have shown the potential to independently serve as prognostic markers for patient survival, on par with established markers such as IDH mutation and MGMT gene methylation status (120). Activated (MC7) or homeostatic (MC2) microglia were associated with improved overall survival, whereas macrophage (MC4) and suppressive bone marrow-derived macrophage (MC3, MC5) signatures were associated with worse survival (120). The data also demonstrate high heterogeneity in types and quantities of immune cells in different regions of the same tumor specimen. Using an experimental mouse glioma model, the authors further demonstrated the critical role of gene expression of a small calcium-binding protein S100A4, which is also considered as a damage-associated molecular pattern (DAMPs) molecule (Box 2). This protein plays a role in GBM-associated T cells and pro-tumorigenic myeloid cells, promoting immunosuppression and glioma growth. The improved survival rate of S100a4^−/− glioma-bearing mice was associated with the generation of anti-tumor immunity, accompanied by increased phagocytic activity of CD11b⁺ glioma-associated myeloid cells and enhanced activity of CD4⁺ T cells, stimulating T cell proliferation and IFNγ secretion (120).

To sum up, deeper investigations into the precise composition of the glioma TME, especially the intricacies of cell-to-cell interactions and their expression patterns, have the potential to facilitate the mitigation of resistance to immunotherapy. This represents an intriguing direction for future research.

3 An emerging role of Th17 cells in glioma microenvironment

In parallel with the immunologically ‘cold’ TME and its high heterogeneity, the peculiarities of T-cell function and the immune environment may lead to controversial effects on tumor progression and patient survival. Specifically, the role of Th17 cells, a subpopulation of effector CD4⁺ helper T cells that produce IL-17A, in the effectiveness of glioma immunotherapy continues to be a subject of extensive discussion. It has been demonstrated that Th17 cells, a subset of IL-17⁺ cells, exhibit a distinct correlation with improved cancer patient survival compared to the overall IL-17⁺ cell population. On the one hand, IL-17 primarily promotes tumorigenesis (113, 154, 155); but on the other, the subpopulation of IL-17 producing Th17 cells have a tumor-suppressing effect. The high level of Th17 cells might mediate the tumor-suppressing effect by facilitating the action of Th1 cells in stimulating and activating cytotoxic T cell immune responses targeting the tumor (113, 156). Another putative mechanism is manifestation of memory stem cell-like properties in cells that differentiate into Th1/Th17 cells and produce IFN-γ (113, 157). On the contrary, transcriptomic data analysis has revealed a strong correlation between Th17 cells and angiogenesis metagenes (158). This suggests that the robust infiltration of Th17 cells into a tumor site possesses the ability to induce VEGF-A secretion, which, in turn, promotes neoangiogenesis and places patients with GBM in unfavorable scenario. Another study has suggested that the inefficiency of immunotherapy in GBM patients may be attributed to the prevalence of Th17 lineage in the CD4⁺ T cell phenotype (159). This prevalence creates a hostile environment for the infiltration of cytotoxic T lymphocytes, potentially through CD39-mediated adenosine production and/or the recruitment of myeloid cells.

Of interest, several studies have provided evidence of the decisive role of T17 cells in the context of anti-cancer strategies based on the immunogenic cell death approach (ICD, Box 2). The use of chemotherapeutic drugs that induce ICD can attract γδT17 cells in the TME, thereby facilitating subsequent cognate anticancer T cell responses (160). Building on this initial work, it has been shown that a dendritic cell (DC) vaccine, based on whole glioma GL261 cell lysates pulsed with ICD-induced photodynamic therapy, has an activated Th17 signature characterized by an increased expression level of Tgfb-3, IL-6, and IL-23a genes (114) (Figure 1). This approach demonstrates prophylactic and therapeutic efficiency in a murine orthotopic glioma model (114). Importantly, blocking the transcription factor responsible for the Th17 response (e.g., RORγt) transforms the TME by depleting IL17 within the tumor which subsequently leads to a significant decrease in the prophylactic efficacy of the dendritic cell (DC) vaccine (114). The design of DC vaccines, based on another ICD-inducer for glioma cell death, leads to an increase in tumoral accumulation of Th1/CTL/Th17 cells in both prophylactic and therapeutic regimens in vivo. This approach is associated with prolonged overall survival in patients with newly diagnosed GBM, as indicated by the conducted meta-analysis (115) (Figure 1).

In conclusion, a solution for the immunologically ‘cold’ TME roadblock and a mechanism transforming the TME into an immune-favorable TME will pave the way for anti-glioma immunotherapy. Given the diverse nature of the TME, one potential approach could involve evaluating its unique composition for each individual patient. However, this remains costly and technically challenging. The application of ICD (Box 2) in the immunotherapeutic approaches against gliomas holds great promise for overcoming T-cell dysfunction in the TME, leading to the activation of specific adaptive immune responses and enhancing ongoing therapy.

4 Which immune profiles are specific to gliomas?

Given the remarkable heterogeneity of glioma and its TME, a more precise identification and characterization of the immune profile would be highly valuable for predicting glioma progression. Additionally, it would aid in predicting patient prognosis, facilitating the selection of personalized treatment regimens and determining the patient’s response to immunotherapy. To date, there is no unified classification for highly accurate determination of the immune profile of a patient diagnosed with glioma to predict the rationale for anti-tumor vaccination and its efficacy. However, in 2018, Thorsson et al. published the results of a comprehensive immunogenomics analysis of more than 10,000 tumors across 33 diverse cancer types in data compiled by TCGA (https://www.cancer.gov/tcga). This analysis identified six major immune subtypes (C1-C6) (Figure 2), which have characteristic patterns of immune responses to predict the prognosis for patients (161). Among the identified immune subtypes, the С4 (lymphocyte depleted) and C5 (immunologically quiet) immune subtypes were the most characteristic for gliomas. The С4 immune subtype is characterized by the prominent macrophage signature, with Th1 suppressed and a high M2 macrophages response, and low lymphocytic infiltrate. Such an immune profile is consistent with an immunosuppressed TME, for which a poor outcome would be expected. The C5 immune subtype is mainly characteristic of low-grade gliomas, the immune profile of which consists of the lowest lymphocyte and highest macrophage responses with high M2 macrophage content. Lower levels of aneuploidy and overall somatic copy number alterations are also typical of С5. High prevalence of IDH mutations (Box 3) suggests their participation in the formation of a TME composition favorable for the outcome (172) and decreased leukocyte chemotaxis, leading to fewer tumor-associated immune cells and better treatment outcome (173).

Figure 2

On the other hand, considerable expansion of the panel of in silico studies has made Thorsson’s classification of gliomas more ambiguous. Bioinformatic immune-associated gene clustering data from TCGA and Chinese Glioma Genome Atlas cohort data (CGGA, http://www.cgga.org.cn/) identified four immune subtypes (IS1-IS4) of gliomas (Figure 2) differing in diverse molecular, cellular and clinical presentation (15). The immune “active” phenotypes IS1 and IS4 are associated with shorter survival and characterized by elevated tumor mutational burden or somatic mutation rates (total numbers of mutations, SNPs and SNVs) and high expression levels of immune checkpoints. This pattern suggests the presence of an immunosuppressive TME that could interfere with the activation of immune responses and the efficacy of ongoing vaccination. On the other hand, the immune “suppressive” phenotypes IS2 and IS3 had lower levels of tumor immune infiltration and elevated expression of ICD regulators, and it was accompanied by good prognosis for patients and their responsiveness to vaccination (15). At the same time, the IS1, IS3 and IS4 phenotypes predominantly overlapped with C1 (wound healing) and C5 (immunologically quiet) immune subtypes of Thorsson’s classification, whereas IS2 overlapped with C2 (IFN-γ dominant) and C4 (lymphocyte depleted) immune profiles. In the IS1 subtype, the additional presence of a large proportion of C6 (TGF-β dominant) was in line with the worst prognosis for patients (15, 161).

Another computational omics data analysis of the TCGA and CGGA cohorts defines three immune subtypes (Ims1-3) associated with the prognosis of diffuse glioma (Figure 2) (162). The Ims1 subtype has main similarities with the C4 immune profile of Thorsson’s classification and is associated with the shortest median survival of patients (14 months). The authors assumed that patients with the Ims1 subtype are more suitable for RNA-based vaccination, as it was characterized by high levels of tumor mutational burden, suggesting that tumor mutations might generate immunogenic neoantigens and a high expression level of immune checkpoints (e.g., PD-1, CD40, PD-L1, CD80 and CD86) and ICD modulators (e.g., FPR1, CXCL10, ANXA1, and MET) (174, 175). The subtype Ims2 was mainly enriched in C4 and C5 of Thorsson’s immune subtypes, whereas the most favorable prognosis (median survival time of 94.5 months) was accompanied by an Ims3 immune profile mainly enriched in C5 immune subtypes (162).

Machine learning-based data analysis of TCGA and CGGA cohorts based on the expression of integrated immune-related gene profiles divides patients with GBM into three immune subtype (IS1-3) and provides a guideline for identification of tumor antigens intended for the design of anti-GBM mRNA vaccines (Figure 2) (163). IS1-3 mainly overlap C4 of Thorsson’s immune profile and is less associated with the C1 and C5 immune profiles. GBM having IS1 (intermediate state) and IS2 (immunologically “hot”) lack regulatory immune cells and immunosuppressive antigen-presenting cells, resulting in T cell activation and the potential for inducing a stronger immune response to ongoing vaccination. IS3 was classified as an immune-cold subtype with the worst prognosis for patients and was characterized by highly complex and problematic TME. Interestingly, the proposed bioinformatics approach allows the identification of intra-cluster heterogeneity in GBMs and subdivides the IS3 subtype into the next three subtypes (IS3A-C), among which IS3A showed significantly better survival than the other two subtypes. IS3A is characterized by a more inflamed microenvironment with more activated B cells, cytotoxic T cells and NK cells.

It is important to develop approaches for precise determination of patient-specific immune profiles and evaluation of the effectiveness of immunotherapeutic strategies against gliomas. Such developments would depend on the development of artificial intelligence and machine learning methods for multi-omics data analysis, which can help to build highly sensitive predictive models. Such models would make it possible to search for specific neoantigens (Box 3) that could form the basis of personalized immunotherapy, including vaccination strategies. However, the bioinformatics predictive models must be confirmed in clinical trials. It is interesting that one advantage of whole-tumour vaccine approaches is that they do not depend on specific haplotypes. Such approaches would allow the immune system to target a wide range of tumor antigens, including any neoantigens that may be presented. This reduces the likelihood of emergence of immune escape variants.

5 Identification of glioma antigans and neoantigens for vaccination

The main goal of the existing vaccination strategies is to identify and target tumor antigens. Identification of mutated and aberrantly expressed intrinsic tumor antigens has historically been a laborious task. Mutant antigens are usually expressed in a tumor-specific manner, and aberrantly expressed antigens are often common in different cancer cell types. Therefore, they are more likely to be the focus of therapeutic anticancer vaccines (Box 3). In 2008, tumor cells genome sequencing revealed that most cancers have somatic mutations (176, 177). In 2012, two independent studies by the groups of Uğur Sahin (178) and Robert Schreiber (179), based on next-generation sequencing and in silico epitope prediction in combination with advanced immunological analysis, proposed a highly accurate algorithm to identify different neoantigens in tumor cells. This approach reduces the time for identifying targeted neoantigens to a few weeks, as opposed to months for more routine antigen cloning approaches. In 2016, Gavin Dunn’s group identified potential targets for generating anti-tumor CD8 T-cell responses in a mouse model of glioblastoma (180). Expressed tumor-specific mutations were characterized by whole exome DNA and RNA sequencing of GL261 cells. Several in silico algorithms for the prediction of MHC I binding were then applied to identify a putative high-affinity H-2Db-restricted neoepitope, and then the immunogenicities of the predicted neoantigens were evaluated. Using IFN-γ Enzyme-Linked Immunospot (ELISPOT) and tetramer assays, the authors confirmed the presence of an endogenous CD8 T-cell response specific for H-2D^b-restricted Imp3D_81N neoantigens. In addition, endogenous T-cell populations neoantigen-specific to Imp3D_81N and Odc1Q_129L in the brain and lymph nodes were detected. This is one of the first studies in which a neoantigen discovery pipeline for the identification of potential therapeutic target candidates was established, providing proof of principle for further analysis of the mechanisms of action of T cell-activating immunotherapeutic approaches in preclinical models of glioblastoma.

More recently, a clinical trial tested personalized neoantigen-targeting vaccines by immunizing patients with newly diagnosed glioblastoma after surgical resection and conventional radiotherapy (phase I/Ib, NCT02287428 ClinicalTrials.gov). The assessment of whole-exome sequencing and RNA-seq data generated from the tumor tissue obtained at the time of diagnostic resection allowed the prediction of personal neoantigens (Box 3) based on analysis of binding affinity to individual HLA alleles. As a result, the personalized vaccine consisted of up to 20 specific peptides of 20–30 amino acids, which generated circulating polyfunctional neoantigen-specific CD4⁺ and CD8⁺ T cell responses enriched in a memory phenotype, along with an increase in intratumoral T cell infiltration (181). Moreover, by using single-cell T cell receptor analysis, the authors demonstrated that neoantigen-specific T cells from the peripheral blood migrate into an intracranial glioblastoma tumor. This indicates that neoantigen-targeting vaccines could favorably change the immune milieu of glioblastoma.

Extensive bioinformatics analysis is currently being applied as a valuable tool to search for glioma-specific neoantigens in patient biomaterial. In a recent study, four antigens, namely ANXA5, FKBP10, MSN and PYGL have been identified as associated with a favorable prognosis for patient and have been proposed as potential target antigens for mRNA vaccine production against glioma (14). Additionally, another four potential antigens including TP53, IDH1, C3 and TCF12, have been identified as significant contributors to glioma growth intensity and have the capacity for direct procession and presentation to CD8 T cells when there is sufficient lymphocyte infiltration to induce an immune response (15). The overexpression of three mutated tumor antigens, KDR, COL1A2, and SAMD9 is associated with the activation of professional antigen-presenting cells (e.g., positive correlation with abundance of B cells, macrophages and DCs) and an unfavorable prognosis for patients with diffuse glioma (162). The authors have shown promising prospects for patients with the Ims-1 immune subtype (Figure 2) for mRNA vaccination targeting these three mentioned antigens (162). Lin et al. have identified six overexpressed and mutated tumor-associated antigens including ARHGAP9, ARHGAP30, CLEC7A, MAN2B1, ARPC1B and PLB1, which are associated with poorer GBM prognosis and increased infiltration of antigen-presenting cells (i.e., B cells, macrophages, and DCs), making them suitable targets for vaccine use (163). It is essential to keep in mind that although some novel glioma antigens have been identified, their therapeutic relevance requires validation in relevant mouse models and in the clinical trial. A recent clinical trial of a peptide vaccine targeting a mutation in codon 132, which encodes the IDH1 gene (IDH1(R132H)), containing a common clonal neoepitope presented on MHC II, has shown promising results, with a three-year survival benefit for patients with astrocytomas (WHO grades 3 and 4, NCT02454634 ClinicalTrials.gov) (182).

At this point, it remains clear that there are several challenges and timeframes associated with the clinical translation of personalized neoantigen-based vaccines. The manufacture of individualized vaccines is a complex and time-consuming process, and there is uncertainty in selecting the optimal platform for neoantigen detection, as well as a lack in consensus regarding the most suitable vaccine delivery platform (160). Moreover, immunoinformatic methods for identifying neoepitopes in cancer genomes are diverse and have not been well-validated (183). Therefore, the clinical development landscape of personalized neoantigen-based vaccines is still evolving. As more clinical trials are conducted, the efficacy and safety of personalized neoantigen-based vaccines will become clearer, and the timeframes for their clinical translation may become more defined. Thus, there is a need to expand our knowledge of glioma antigens and neoantigens, as well as the methods for their identification. Many more interesting and challenging findings are expected, along with potentially applicable therapeutic approaches in the field.

6 Concluding remarks and future perspectives

Immunotherapy is currently considered an integral part of complex therapeutic procedures designed to increase the curability of a wide range of cancers. The involvement of immune cells and activation of a T-cell immune response allows the effective eradication of tumor cells that remain viable after conventional therapy, eliminating the risk of secondary tumor development and metastasis. Several immunotherapeutic strategies are currently used and adapted for the anti-cancer treatment (e.g., immune checkpoint inhibitors, chimeric antigen receptor (CAR) T cell therapy, oncolytic virus-based therapy, gene-mediated cytotoxic immunotherapy, and therapeutic anti-tumor vaccination). But approaches based on the induction of ICD (Box 2) draw a lot of attention, as they can act as a powerful tool to multiply immunotherapeutic efficacy. However, despite great promise and abundant evidence supporting the necessity for immunotherapy, its application in glioma treatment has yet to achieve major breakthroughs in the clinic. This is due to a number of challenges that need to be addressed to achieve enhanced immunotherapy against gliomas.

The main challenge in glioma immunotherapy is the high hostility of glioma TME to T-cell infiltration and activation. Gliomas, particularly high-grade gliomas like glioblastoma multiforme, exhibit an immunologically “cold tumor” phenotype characterized by high TME heterogeneity, as well as tumor cell-intrinsic mutations and tumor-extrinsic mechanisms (106) (Figure 1). Recent data have highlighted the significant potential of inducing ICD in glioma, which leads to the recruitment of Th17 cells to the TME, thereby transforming it into an immunologically “hot” environment. This activation of antitumor immunity fosters an effective response to ongoing immunotherapy (114, 115). Therefore, it is reasonable to anticipate that the ICD approach will pave the way for improved immunotherapy against glioma.

During the analysis of the immune profiles of patients with gliomas (Figure 2), it becomes evident that the significant heterogeneity of gliomas and their TME excludes the possibility of a unified classification of a patient’s immunological profile. Development of a unified classification will be helpful for accurate assessment of tumor progression and responsiveness to therapy. Strictly speaking, it seems that a personalized immunological profile needs to be defined for each glioma patient. In this regard, different artificial intelligence and machine learning methods for multi-omics data analysis are being actively developed to build highly sensitive predictive models that can search for specific neoantigens (Box 3) for personalized immunotherapy, including vaccination strategies. However, it will take some time to verify them in vivo and even more so in clinical trials. Nevertheless, looking back at the currently accepted Thorsson’s classification (Figure 2), it can already be noted that increased expression of Th17 and Th1 genes is one of the essential components for best prognosis.

Due to the high heterogeneity of gliomas, there is significant interest in exploring novel approaches to identify neoantigens derived from specific tumor mutations unique to each patient (166–168). Personalized therapeutic vaccination, utilizing the patient’s own tumor tissue based on these identified personalized neoantigens, represents a fundamentally new therapeutic option for glioma patients and could increase the effectiveness of specific antitumor immunity (170, 171). Remarkably, in the design of vaccines against glioma, it is possible to combine multiple mRNA sequences of patient-specific neoantigens with mRNA sequences of adjuvants, potentially boosting the antitumor immune response. A well-known technology, TriMix, includes mRNA encoding costimulatory molecules such as CD70 and CD40 ligand (CD40L), and active TLR4, and it is being enhanced to facilitate effective activation of DC cross-presentation and priming of CD8⁺ T cell responses (184–187). Alternatively, mRNA sequences of cytokine cocktail (e.g., IL-23, IL-36γ) and the T cell costimulatory OX40L, both actively involved in combating the immunosuppressive TME and promoting durable anticancer immunity (188), can be employed as independent adjuvants or in combination with the TriMix technology. Further development of this approach in glioma immunotherapy will open new avenues for the creation of more effective therapeutic regimens. These regiments will be tailored to analyze individual rare immunogenic mutations, leading to the most personalized treatment strategies.

References

• 1

  OstromQTPriceMNeffCCioffiGWaiteKAKruchkoCet al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2015-2019. Neuro Oncol (2022) 24(Suppl 5):v1–v95. doi: 10.1093/neuonc/noac202

  • CrossRef
  • Google Scholar

• 2

  RajRSeppäKLuostarinenTMalilaNSeppäläMPitkäniemiJet al. Disparities in glioblastoma survival by case volume: a nationwide observational study. J Neurooncol. (2020) 147(2):361–70. doi: 10.1007/s11060-020-03428-5

  • CrossRef
  • Google Scholar

• 3

  TykockiTEltayebM. Ten-year survival in glioblastoma. A systematic review. J Clin Neurosci (2018) 54:7–13. doi: 10.1016/j.jocn.2018.05.002

  • CrossRef
  • Google Scholar

• 4

  OstromQTCoteDJAschaMKruchkoCBarnholtz-SloanJS. Adult glioma incidence and survival by race or ethnicity in the United States from 2000 to 2014. JAMA Oncol (2018) 4(9):1254–62. doi: 10.1001/jamaoncol.2018.1789

  • CrossRef
  • Google Scholar

• 5

  Barnholtz-SloanJSOstromQTCoteD. Epidemiology of brain tumors. Neurol Clin (2018) 36(3):395–419. doi: 10.1016/j.ncl.2018.04.001

  • CrossRef
  • Google Scholar

• 6

  LouisDNPerryAWesselingPBratDJCreeIAFigarella-BrangerDet al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-Oncology (2021) 23(8):1231–51. doi: 10.1093/neuonc/noab106

  • CrossRef
  • Google Scholar

• 7

  KomoriT. The 2021 WHO classification of tumors, 5th edition, central nervous system tumors: the 10 basic principles. Brain Tumor Pathol (2022) 39(2):47–50. doi: 10.1007/s10014-022-00428-3

  • CrossRef
  • Google Scholar

• 8

  KomoriT. Grading of adult diffuse gliomas according to the 2021 WHO Classification of Tumors of the Central Nervous System. Lab Invest. (2022) 102(2):126–33. doi: 10.1038/s41374-021-00667-6

  • CrossRef
  • Google Scholar

• 9

  PerryAWesselingP. Histologic classification of gliomas. Handb Clin Neurol (2016) 134:71–95. doi: 10.1016/B978-0-12-802997-8.00005-0

  • CrossRef
  • Google Scholar

• 10

  YangKWuZZhangHZhangNWuWWangZet al. Glioma targeted therapy: insight into future of molecular approaches. Mol Cancer. (2022) 21(1):39. doi: 10.1186/s12943-022-01513-z

  • CrossRef
  • Google Scholar

• 11

  ŚledzińskaPBebynMGFurtakJKowalewskiJLewandowskaMA. Prognostic and predictive biomarkers in gliomas. Int J Mol Sci (2021) 22(19):1–32. doi: 10.3390/ijms221910373

  • CrossRef
  • Google Scholar

• 12

  LouisDNPerryAReifenbergerGvon DeimlingAFigarella-BrangerDCaveneeWKet al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathologica. (2016) 131(6):803–20. doi: 10.1007/s00401-016-1545-1

  • CrossRef
  • Google Scholar

• 13

  Martinez-LageMLynchTMBiYCocitoCWayGPPalSet al. Immune landscapes associated with different glioblastoma molecular subtypes. Acta Neuropathologica Commun (2019) 7(1):203. doi: 10.1186/s40478-019-0803-6

  • CrossRef
  • Google Scholar

• 14

  ZhongHLiuSCaoFZhaoYZhouJTangFet al. Dissecting tumor antigens and immune subtypes of glioma to develop mRNA vaccine. Front Immunol (2021) 12:709986. doi: 10.3389/fimmu.2021.709986

  • CrossRef
  • Google Scholar

• 15

  MaSBaYJiHWangFDuJHuS. Recognition of tumor-associated antigens and immune subtypes in glioma for mRNA vaccine development. Front Immunol (2021) 12:738435. doi: 10.3389/fimmu.2021.738435

  • CrossRef
  • Google Scholar

• 16

  WangYLuoRZhangXXiangHYangBFengJet al. Proteogenomics of diffuse gliomas reveal molecular subtypes associated with specific therapeutic targets and immune-evasion mechanisms. Nat Commun (2023) 14(1):505. doi: 10.1038/s41467-023-36005-1

  • CrossRef
  • Google Scholar

• 17

  LinWGaoJZhangHChenLQiuXHuangQet al. Identification of molecular subtypes based on inflammatory response in lower-grade glioma. Inflammation Regeneration. (2022) 42(1):29. doi: 10.1186/s41232-022-00215-9

  • CrossRef
  • Google Scholar

• 18

  FengPLiZLiYZhangYMiaoX. Characterization of different subtypes of immune cell infiltration in glioblastoma to aid immunotherapy. Front Immunol (2022) 13:799509. doi: 10.3389/fimmu.2022.799509

  • CrossRef
  • Google Scholar

• 19

  VogazianouAPChanRBäcklundLMPearsonDMLiuLLangfordCFet al. Distinct patterns of 1p and 19q alterations identify subtypes of human gliomas that have different prognoses. Neuro Oncol (2010) 12(7):664–78. doi: 10.1093/neuonc/nop075

  • CrossRef
  • Google Scholar

• 20

  MolinaroAMTaylorJWWienckeJKWrenschMR. Genetic and molecular epidemiology of adult diffuse glioma. Nat Rev Neurol (2019) 15(7):405–17. doi: 10.1038/s41582-019-0220-2

  • CrossRef
  • Google Scholar

• 21

  BergerTRWenPYLang-OrsiniMChukwuekeUN. World Health Organization 2021 classification of central nervous system tumors and implications for therapy for adult-type gliomas: A review. JAMA Oncol (2022) 8(10):1493–501. doi: 10.1001/jamaoncol.2022.2844

  • CrossRef
  • Google Scholar

• 22

  WhitfieldBTHuseJT. Classification of adult-type diffuse gliomas: Impact of the World Health Organization 2021 update. Brain Pathol (2022) 32(4):e13062. doi: 10.1111/bpa.13062

  • CrossRef
  • Google Scholar

• 23

  RichardQLaurengeAMallatMSansonMCastro-VegaLJ. New insights into the Immune TME of adult-type diffuse gliomas. Curr Opin Neurol (2022) 35(6):794–802. doi: 10.1097/WCO.0000000000001112

  • CrossRef
  • Google Scholar

• 24

  MulhollandSPearsonDMHamoudiRAMalleyDSSmithCMWeaverJMet al. MGMT CpG island is invariably methylated in adult astrocytic and oligodendroglial tumors with IDH1 or IDH2 mutations. Int J Cancer. (2012) 131(5):1104–13. doi: 10.1002/ijc.26499

  • CrossRef
  • Google Scholar

• 25

  PerryJRLaperriereNO'CallaghanCJBrandesAAMentenJPhillipsCet al. Short-course radiation plus temozolomide in elderly patients with glioblastoma. N Engl J Med (2017) 376(11):1027–37. doi: 10.1056/NEJMoa1611977

  • CrossRef
  • Google Scholar

• 26

  KarschniaPVogelbaumMAvan den BentMCahillDPBelloLNaritaYet al. Evidence-based recommendations on categories for extent of resection in diffuse glioma. Eur J Cancer. (2021) 149:23–33. doi: 10.1016/j.ejca.2021.03.002

  • CrossRef
  • Google Scholar

• 27

  WellerMvan den BentMPreusserMLe RhunETonnJCMinnitiGet al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol (2021) 18(3):170–86. doi: 10.1038/s41571-020-00447-z

  • CrossRef
  • Google Scholar

• 28

  QaddoumiIOrismeWWenJSantiagoTGuptaKDaltonJDet al. Genetic alterations in uncommon low-grade neuroepithelial tumors: BRAF, FGFR1, and MYB mutations occur at high frequency and align with morphology. Acta Neuropathol. (2016) 131(6):833–45. doi: 10.3322/caac.21613

  • CrossRef
  • Google Scholar

• 29

  KomoriT. The molecular framework of pediatric-type diffuse gliomas: shifting toward the revision of the WHO classification of tumors of the central nervous system. Brain Tumor Pathol (2021) 38(1):1–3. doi: 10.1016/S1470-2045(06)70665-9

  • CrossRef
  • Google Scholar

• 30

  ChiangJHarreldJHTinkleCLMoreiraDCLiXAcharyaSet al. A single-center study of the clinicopathologic correlates of gliomas with a MYB or MYBL1 alteration. Acta Neuropathol. (2019) 138(6):1091–2. doi: 10.1007/s11060-018-2956-8

  • CrossRef
  • Google Scholar

• 31

  EllisonDWHawkinsCJonesDTOnar-ThomasAPfisterSMReifenbergerGet al. cIMPACT-NOW update 4: diffuse gliomas characterized by MYB, MYBL1, or FGFR1 alterations or BRAF V600E mutation. Acta Neuropathologica. (2019) 137:683–7. doi: 10.3389/fonc.2020.01069

  • CrossRef
  • Google Scholar

• 32

  RyallSTaboriUHawkinsC. Pediatric low-grade glioma in the era of molecular diagnostics. Acta Neuropathol Commun (2020) 8(1):30. doi: 10.1093/neuros/nyaa475

  • CrossRef
  • Google Scholar

• 33

  JohnsonDRGianniniCJenkinsRBKimDKKaufmannTJ. Plenty of calcification: imaging characterization of polymorphous low-grade neuroepithelial tumor of the young. Neuroradiology (2019) 61(11):1327–32. doi: 10.1227/NEU.0b013e31823f5ade

  • CrossRef
  • Google Scholar

• 34

  BensonJCSummerfieldDCarrCCogswellPMessinaSGompelJVet al. Polymorphous low-grade neuroepithelial tumor of the young as a partially calcified intra-axial mass in an adult. AJNR Am J Neuroradiol (2020) 41(4):573–8. doi: 10.1007/s11060-016-2110-4

  • CrossRef
  • Google Scholar

• 35

  ChenYTianTGuoXZhangFFanMJinHet al. Polymorphous low-grade neuroepithelial tumor of the young: case report and review focus on the radiological features and genetic alterations. BMC Neurol (2020) 20(1):123. doi: 10.3389/fneur.2021.658680

  • CrossRef
  • Google Scholar

• 36

  CastelDPhilippeCCalmonRLe DretLTruffauxNBoddaertNet al. Histone H3F3A and HIST1H3B K27M mutations define two subgroups of diffuse intrinsic pontine gliomas with different prognosis and phenotypes. Acta Neuropathol. (2015) 130(6):815–27. doi: 10.1093/neuonc/nor133

  • CrossRef
  • Google Scholar

• 37

  LouisDNOhgakiHWiestlerODCaveneeWKBurgerPCJouvetAet al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. (2007) 114(2):97–109. doi: 10.1016/S1470-2045(11)70196-6

  • CrossRef
  • Google Scholar

• 38

  Tauziède-EspariatADebilyMACastelDGrillJPugetSSabelMet al. An integrative radiological, histopathological and molecular analysis of pediatric pontine histone-wildtype glioma with MYCN amplification (HGG-MYCN). Acta Neuropathol Commun (2019) 7(1):87. doi: 10.1186/s40478-019-0738-y

  • CrossRef
  • Google Scholar

• 39

  Tauziède-EspariatADebilyMACastelDGrillJPugetSRouxAet al. The pediatric supratentorial MYCN-amplified high-grade gliomas methylation class presents the same radiological, histopathological and molecular features as their pontine counterparts. Acta Neuropathol Commun (2020) 8(1):104. doi: 10.1038/nrc2246

  • CrossRef
  • Google Scholar

• 40

  Guerreiro StucklinASRyallSFukuokaKZapotockyMLassalettaALiCet al. Alterations in ALK/ROS1/NTRK/MET drive a group of infantile hemispheric gliomas. Nat Commun (2019) 10(1):4343. doi: 10.1038/srep24912

  • CrossRef
  • Google Scholar

• 41

  ClarkeMMackayAIsmerBPicklesJCTatevossianRGNewmanSet al. Infant high-grade gliomas comprise multiple subgroups characterized by novel targetable gene fusions and favorable outcomes. Cancer Discovery (2020) 10(7):942–63. doi: 10.1056/NEJMoa043330

  • CrossRef
  • Google Scholar

• 42

  LimKYWonJKParkCKKimSKChoiSHKimTet al. H3 G34-mutant high-grade glioma. Brain Tumor Pathol (2021) 38(1):4–13. doi: 10.1016/S1470-2045(17)30194-8

  • CrossRef
  • Google Scholar

• 43

  KorshunovACapperDReussDSchrimpfDRyzhovaMHovestadtVet al. Histologically distinct neuroepithelial tumors with histone 3 G34 mutation are molecularly similar and comprise a single nosologic entity. Acta Neuropathol. (2016) 131(1):137–46. doi: 10.1093/neuonc/nox002

  • CrossRef
  • Google Scholar

• 44

  AlRayahiJZapotockyMRamaswamyVHanagandiPBransonHMubarakWet al. Pediatric brain tumor genetics: what radiologists need to know. Radiographics (2018) 38(7):2102–22. doi: 10.1177/1533033818806498

  • CrossRef
  • Google Scholar

• 45

  BenderKPerezEChiricaMOnkenJKahnJBrennerWet al. High-grade astrocytoma with piloid features (HGAP): the Charité experience with a new central nervous system tumor entity. J Neurooncol. (2021) 153(1):109–20. doi: 10.3892/or.2022.8312

  • CrossRef
  • Google Scholar

• 46

  BiczokAStrübingFLEderJMEgenspergerRSchnellOZausingerSet al. Molecular diagnostics helps to identify distinct subgroups of spinal astrocytomas. Acta Neuropathol Commun (2021) 9(1):119. doi: 10.1093/mutage/17.6.483

  • CrossRef
  • Google Scholar

• 47

  Dias-SantagataDLamQVernovskyKVenaNLennerzJKBorgerDRet al. BRAF V600E mutations are common in pleomorphic xanthoastrocytoma: diagnostic and therapeutic implications. PloS One (2011) 6(3):e17948. doi: 10.1200/JCO.2002.20.5.1375

  • CrossRef
  • Google Scholar

• 48

  DrevelegasA. Imaging of Brain Tumors with Histological Correlations. Berlin Heidelberg: Springer (2010).

  • Google Scholar

• 49

  BongettaDRissoAMorbiniPButtiGGaetaniP. Chordoid glioma: a rare radiologically, histologically, and clinically mystifying lesion. World J Surg Oncol (2015) 13:188. doi: 10.1016/j.dnarep.2019.04.007

  • CrossRef
  • Google Scholar

• 50

  Beni-AdaniLGomoriMSpektorSConstantiniS. Cyst wall enhancement in pilocytic astrocytoma: neoplastic or reactive phenomena. Pediatr Neurosurg (2000) 32(5):234–9. doi: 10.1002/jcp.25896

  • CrossRef
  • Google Scholar

• 51

  KoellerKKRushingEJ. From the archives of the AFIP: pilocytic astrocytoma: radiologic-pathologic correlation. Radiographics (2004) 24(6):1693–708. doi: 10.1002/cam4.3217

  • CrossRef
  • Google Scholar

• 52

  RippeDJBoykoOBRadiMWorthRFullerGN. MRI of temporal lobe pleomorphic xanthoastrocytoma. J Comput Assist Tomogr. (1992) 16(6):856–9. doi: 10.1016/j.gendis.2016.04.007

  • CrossRef
  • Google Scholar

• 53

  PomperMGPasseTJBurgerPCScheithauerBWBratDJ. Chordoid glioma: a neoplasm unique to the hypothalamus and anterior third ventricle. AJNR Am J Neuroradiol (2001) 22(3):464–9. doi: 10.1186/s12929-021-00717-7

  • CrossRef
  • Google Scholar

• 54

  ChenXZhangBPanSSunQBianL. Chordoid glioma of the third ventricle: A case report and a treatment strategy to this rare tumor. Front Oncol (2020) 10:502. doi: 10.1200/JCO.2022.40.16_suppl.e14037

  • CrossRef
  • Google Scholar

• 55

  GopakumarSMcDonaldMFSharmaHTatsuiCEFullerGNRaoG. Recurrent HGNET-MN1 altered (astroblastoma MN1-altered) of the foramen magnum: Case report and molecular classification. Surg Neurol Int (2022) 13:139. doi: 10.1056/NEJMoa1500925

  • CrossRef
  • Google Scholar

• 56

  TanACAshleyDMLópezGYMalinzakMFriedmanHSKhasrawM. Management of glioblastoma: State of the art and future directions. CA Cancer J Clin (2020) 70(4):299–312. doi: 10.1016/j.ctrv.2020.102029

  • CrossRef
  • Google Scholar

• 57

  StummerWPichlmeierUMeinelTWiestlerODZanellaFReulenHJ. Fluorescence-guided surgery with 5-aminolevulinic acid for resection of Malignant glioma: a randomised controlled multicentre phase III trial. Lancet Oncol (2006) 7(5):392–401. doi: 10.1111/cas.15141

  • CrossRef
  • Google Scholar

• 58

  ChohanMOBergerMS. 5-Aminolevulinic acid fluorescence guided surgery for recurrent high-grade gliomas. J Neurooncol. (2019) 141(3):517–22. doi: 10.3390/ijms22105111

  • CrossRef
  • Google Scholar

• 59

  CharalampakiPProskynitopoulosPJHeimannANakamuraM. 5-aminolevulinic acid multispectral imaging for the fluorescence-guided resection of brain tumors: A prospective observational study. Front Oncol (2020) 10:1069. doi: 10.1016/j.ejphar.2014.07.025

  • CrossRef
  • Google Scholar

• 60

  OrillacCStummerWOrringerDA. Fluorescence guidance and intraoperative adjuvants to maximize extent of resection. Neurosurgery (2021) 89(5):727–36. doi: 10.3389/fphar.2020.00343

  • CrossRef
  • Google Scholar

• 61

  KriegSMShibanEDroeseDGemptJBuchmannNPapeHet al. Predictive value and safety of intraoperative neurophysiological monitoring with motor evoked potentials in glioma surgery. Neurosurgery (2012) 70(5):1060–70. doi: 10.1016/j.cytogfr.2013.01.005

  • CrossRef
  • Google Scholar

• 62

  Hervey-JumperSLBergerMS. Maximizing safe resection of low- and high-grade glioma. J Neurooncol. (2016) 130(2):269–82. doi: 10.1038/nrc3380

  • CrossRef
  • Google Scholar

• 63

  YouHQiaoH. Intraoperative neuromonitoring during resection of gliomas involving eloquent areas. Front Neurol (2021) 12:658680. doi: 10.1212/01.wnl.0000197792.73656.c2

  • CrossRef
  • Google Scholar

• 64

  KuhntDBeckerAGanslandtOBauerMBuchfelderMNimskyC. Correlation of the extent of tumor volume resection and patient survival in surgery of glioblastoma multiforme with high-field intraoperative MRI guidance. Neuro Oncol (2011) 13(12):1339–48. doi: 10.1002/14651858.CD011773.pub2

  • CrossRef
  • Google Scholar

• 65

  SenftCBinkAFranzKVatterHGasserTSeifertV. Intraoperative MRI guidance and extent of resection in glioma surgery: a randomised, controlled trial. Lancet Oncol (2011) 12(11):997–1003. doi: 10.3389/fonc.2021.711088

  • CrossRef
  • Google Scholar

• 66

  SinghSKClarkeIDTerasakiMBonnVEHawkinsCSquireJet al. Identification of a cancer stem cell in human brain tumors. Cancer Res (2003) 63(18):5821–8. doi: 10.1038/s41591-018-0337-7

  • CrossRef
  • Google Scholar

• 67

  GilbertsonRJRichJN. Making a tumour's bed: glioblastoma stem cells and the vascular niche. Nat Rev Cancer. (2007) 7(10):733–6. doi: 10.1002/cncr.33378

  • CrossRef
  • Google Scholar

• 68

  ChaJKangSGKimP. Strategies of mesenchymal invasion of patient-derived brain tumors: microenvironmental adaptation. Sci Rep (2016) 6:24912. doi: 10.1001/jamaoncol.2020.1024

  • CrossRef
  • Google Scholar

• 69

  StuppRMasonWPvan den BentMJWellerMFisherBTaphoornMJet al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med (2005) 352(10):987–96. doi: 10.1056/NEJMoa1610497

  • CrossRef
  • Google Scholar

• 70

  WellerMvan den BentMTonnJCStuppRPreusserMCohen-Jonathan-MoyalEet al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol (2017) 18(6):e315–e29. doi: 10.1126/scitranslmed.aaw2672

  • CrossRef
  • Google Scholar

• 71

  GrosshansDRMohanRGondiVShihHAMahajanABrownPD. The role of image-guided intensity modulated proton therapy in glioma. Neuro Oncol (2017) 19(suppl_2):ii30–ii7. doi: 10.1038/s41586-022-04489-4

  • CrossRef
  • Google Scholar

• 72

  ReynaudTBertautAFarahWThibouwDCrehangeGTrucGet al. Hypofractionated stereotactic radiotherapy as a salvage therapy for recurrent high-grade gliomas: single-center experience. Technol Cancer Res Treat (2018) 17:1533033818806498. doi: 10.1056/NEJMoa1716435

  • CrossRef
  • Google Scholar

• 73

  ZhouYSWangWChenNWangLCHuangJB. Research progress of anti-glioma chemotherapeutic drugs (Review). Oncol Rep (2022) 47(5):1–12. doi: 10.1056/NEJMoa2024947

  • CrossRef
  • Google Scholar

• 74

  MargisonGPSantibáñez KorefMFPoveyAC. Mechanisms of carcinogenicity/chemotherapy by O6-methylguanine. Mutagenesis (2002) 17(6):483–7. doi: 10.1093/neuonc/now002

  • CrossRef
  • Google Scholar

• 75

  StuppRDietrichPYOstermann KraljevicSPicaAMaillardIMaederPet al. Promising survival for patients with newly diagnosed glioblastoma multiforme treated with concomitant radiation plus temozolomide followed by adjuvant temozolomide. J Clin Oncol (2002) 20(5):1375–82. doi: 10.1186/s13046-020-01778-6

  • CrossRef
  • Google Scholar

• 76

  DanielPSabriSChaddadAMeehanBJean-ClaudeBRakJet al. Temozolomide induced hypermutation in glioma: evolutionary mechanisms and therapeutic opportunities. Front Oncol (2019) 9:41. doi: 10.1038/nrneurol.2017.64

  • CrossRef
  • Google Scholar

• 77

  KainaBChristmannM. DNA repair in personalized brain cancer therapy with temozolomide and nitrosoureas. DNA Repair (Amst). (2019) 78:128–41. doi: 10.1158/1078-0432.CCR-13-0551

  • CrossRef
  • Google Scholar

• 78

  BinabajMMBahramiAShahidSalesSJoodiMJoudi MashhadMHassanianSMet al. The prognostic value of MGMT promoter methylation in glioblastoma: A meta-analysis of clinical trials. J Cell Physiol (2018) 233(1):378–86. doi: 10.1038/s41590-019-0433-y

  • CrossRef
  • Google Scholar

• 79

  RichardSTachonGMilinSWagerMKarayan-TaponL. Dual MGMT inactivation by promoter hypermethylation and loss of the long arm of chromosome 10 in glioblastoma. Cancer Med (2020) 9(17):6344–53. doi: 10.3390/ijms19102879

  • CrossRef
  • Google Scholar

• 80

  LeeSY. Temozolomide resistance in glioblastoma multiforme. Genes Dis (2016) 3(3):198–210. doi: 10.3389/fimmu.2022.964898

  • CrossRef
  • Google Scholar

• 81

  ChienCHHsuehWTChuangJYChangKY. Dissecting the mechanism of temozolomide resistance and its association with the regulatory roles of intracellular reactive oxygen species in glioblastoma. J BioMed Sci (2021) 28(1):18. doi: 10.1038/s41593-020-00789-y

  • CrossRef
  • Google Scholar

• 82

  McmahonDHusseinAMangleburgHNichianainAFitzpatrickOMcLaughlinRAet al. Toxicity of concurrent and adjuvant temozolomide in patients with glioblastoma multiforme (GBM). Am Soc Clin Oncol (2022) e14037. doi: 10.1111/nan.12428

  • CrossRef
  • Google Scholar

• 83

  BucknerJCShawEGPughSLChakravartiAGilbertMRBargerGRet al. Radiation plus procarbazine, CCNU, and vincristine in low-grade glioma. N Engl J Med (2016) 374(14):1344–55. doi: 10.1038/s41568-021-00397-3

  • CrossRef
  • Google Scholar

• 84

  WellerMLe RhunE. How did lomustine become standard of care in recurrent glioblastoma? Cancer Treat Rev (2020) 87:102029. doi: 10.1038/s41467-022-28523-1

  • CrossRef
  • Google Scholar

• 85

  YamamuroSTakahashiMSatomiKSasakiNKobayashiTUchidaEet al. Lomustine and nimustine exert efficient antitumor effects against glioblastoma models with acquired temozolomide resistance. Cancer Sci (2021) 112(11):4736–47. doi: 10.1111/cas.15141

  • CrossRef
  • Google Scholar

• 86

  JeonJLeeSKimHKangHYounHJoSet al. Revisiting platinum-based anticancer drugs to overcome gliomas. Int J Mol Sci (2021) 22(10):1–20. doi: 10.1038/s41419-022-05514-0

  • CrossRef
  • Google Scholar

• 87

  DasariSTchounwouPB. Cisplatin in cancer therapy: molecular mechanisms of action. Eur J Pharmacol (2014) 740:364–78. doi: 10.1126/scitranslmed.aae0105

  • CrossRef
  • Google Scholar

• 88

  ZhouJKangYChenLWangHLiuJZengSet al. The drug-resistance mechanisms of five platinum-based antitumor agents. Front Pharmacol (2020) 11:343. doi: 10.1158/1078-0432.CCR-18-1627

  • CrossRef
  • Google Scholar

• 89

  DudekAMGargADKryskoDVDe RuysscherDAgostinisP. Inducers of immunogenic cancer cell death. Cytokine Growth Factor Rev (2013) 24(4):319–33. doi: 10.3389/fimmu.2022.1011757

  • CrossRef
  • Google Scholar

• 90

  KryskoDVGargADKaczmarekAKryskoOAgostinisPVandenabeeleP. Immunogenic cell death and DAMPs in cancer therapy. Nat Rev Cancer. (2012) 12(12):860–75. doi: 10.1038/s41467-021-21407-w

  • CrossRef
  • Google Scholar

• 91

  SchmidtFFischerJHerrlingerUDietzKDichgansJWellerM. PCV chemotherapy for recurrent glioblastoma. Neurology (2006) 66(4):587–9. doi: 10.1016/j.ccell.2022.05.009

  • CrossRef
  • Google Scholar

• 92

  ParasramkaSTalariGRosenfeldMGuoJVillanoJL. Procarbazine, lomustine and vincristine for recurrent high-grade glioma. Cochrane Database Syst Rev (2017) 7(7):Cd011773. doi: 10.1038/s41467-022-28372-y

  • CrossRef
  • Google Scholar

• 93

  LiaoWHHsiaoMYKungYHuangAPChenWS. Investigation of the therapeutic effect of doxorubicin combined with focused shockwave on glioblastoma. Front Oncol (2021) 11:711088. doi: 10.3892/ol.2013.1518

  • CrossRef
  • Google Scholar

• 94

  CloughesyTFMochizukiAYOrpillaJRHugoWLeeAHDavidsonTBet al. Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma. Nat Med (2019) 25(3):477–86. doi: 10.1182/blood-2002-05-1461

  • CrossRef
  • Google Scholar

• 95

  ReardonDAKimTMFrenelJSSimonelliMLopezJSubramaniamDSet al. Treatment with pembrolizumab in programmed death ligand 1-positive recurrent glioblastoma: Results from the multicohort phase 1 KEYNOTE-028 trial. Cancer (2021) 127(10):1620–9. doi: 10.1182/blood.V99.6.2114

  • CrossRef
  • Google Scholar

• 96

  ReardonDABrandesAAOmuroAMulhollandPLimMWickAet al. Effect of nivolumab vs bevacizumab in patients with recurrent glioblastoma: the checkMate 143 phase 3 randomized clinical trial. JAMA Oncol (2020) 6(7):1003–10. doi: 10.3389/fimmu.2014.00276

  • CrossRef
  • Google Scholar

• 97

  BrownCEAlizadehDStarrRWengLWagnerJRNaranjoAet al. Regression of glioblastoma after chimeric antigen receptor T-cell therapy. N Engl J Med (2016) 375(26):2561–9. doi: 10.1080/2162402X.2017.1321186

  • CrossRef
  • Google Scholar

• 98

  WangDStarrRChangWCAguilarBAlizadehDWrightSLet al. Chlorotoxin-directed CAR T cells for specific and effective targeting of glioblastoma. Sci Transl Med (2020) 12(533):1–14. doi: 10.1126/scitranslmed.aaw2672

  • CrossRef
  • Google Scholar

• 99

  MajznerRGRamakrishnaSYeomKWPatelSChinnasamyHSchultzLMet al. GD2-CAR T cell therapy for H3K27M-mutated diffuse midline gliomas. Nature (2022) 603(7903):934–41. doi: 10.1084/jem.20100269

  • CrossRef
  • Google Scholar

• 100

  DesjardinsAGromeierMHerndonJE2ndBeaubierNBolognesiDPFriedmanAHet al. Recurrent glioblastoma treated with recombinant poliovirus. N Engl J Med (2018) 379(2):150–61. doi: 10.1016/j.immuni.2018.03.023

  • CrossRef
  • Google Scholar

• 101

  FriedmanGKJohnstonJMBagAKBernstockJDLiRAbanIet al. Oncolytic HSV-1 G207 immunovirotherapy for pediatric high-grade gliomas. N Engl J Med (2021) 384(17):1613–22. doi: 10.7150/thno.61677

  • CrossRef
  • Google Scholar

• 102

  WheelerLAManzaneraAGBellSDCavaliereRMcGregorJMGreculaJCet al. Phase II multicenter study of gene-mediated cytotoxic immunotherapy as adjuvant to surgical resection for newly diagnosed Malignant glioma. Neuro Oncol (2016) 18(8):1137–45. doi: 10.3389/fimmu.2022.773264

  • CrossRef
  • Google Scholar

• 103

  YanYZengSGongZXuZ. Clinical implication of cellular vaccine in glioma: current advances and future prospects. J Exp Clin Cancer Res (2020) 39(1):257. doi: 10.1126/science.aai8478

  • CrossRef
  • Google Scholar

• 104

  WellerMRothPPreusserMWickWReardonDAPlattenMet al. Vaccine-based immunotherapeutic approaches to gliomas and beyond. Nat Rev Neurol (2017) 13(6):363–74. doi: 10.1101/gad.294991.116

  • CrossRef
  • Google Scholar

• 105

  RutledgeWCKongJGaoJGutmanDACooperLAAppinCet al. Tumor-infiltrating lymphocytes in glioblastoma are associated with specific genomic alterations and related to transcriptional class. Clin Cancer Res (2013) 19(18):4951–60. doi: 10.1007/s00401-021-02337-9

  • CrossRef
  • Google Scholar

• 106

  JacksonCMChoiJLimM. Mechanisms of immunotherapy resistance: lessons from glioblastoma. Nat Immunol (2019) 20(9):1100–9. doi: 10.1038/s41586-021-03580-6

  • CrossRef
  • Google Scholar

• 107

  Da RosMDe GregorioVIorioALGiuntiLGuidiMde MartinoMet al. Glioblastoma chemoresistance: the double play by microenvironment and blood-brain barrier. Int J Mol Sci (2018) 19(10):1–23. doi: 10.1038/nature07485

  • CrossRef
  • Google Scholar

• 108

  WangGZhongKWangZZhangZTangXTongAet al. Tumor-associated microglia and macrophages in glioblastoma: From basic insights to therapeutic opportunities. Front Immunol (2022) 13:964898. doi: 10.1158/0008-5472.CAN-07-3095

  • CrossRef
  • Google Scholar

• 109

  Pombo AntunesARScheyltjensILodiFMessiaenJAntoranzADuerinckJet al. Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. Nat Neurosci (2021) 24(4):595–610. doi: 10.1158/0008-5472.CAN-11-3722

  • CrossRef
  • Google Scholar

• 110

  SørensenMDDahlrotRHBoldtHBHansenSKristensenBW. Tumour-associated microglia/macrophages predict poor prognosis in high-grade gliomas and correlate with an aggressive tumour subtype. Neuropathol Appl Neurobiol (2018) 44(2):185–206. doi: 10.1038/nature10755

  • CrossRef
  • Google Scholar

• 111

  AndersenBMFaust AklCWheelerMAChioccaEAReardonDAQuintanaFJ. Glial and myeloid heterogeneity in the brain tumour microenvironment. Nat Rev Cancer. (2021) 21(12):786–802. doi: 10.1158/2326-6066.CIR-16-0156

  • CrossRef
  • Google Scholar

• 112

  RaviVMNeidertNWillPJosephKMaierJPKückelhausJet al. T-cell dysfunction in the glioblastoma microenvironment is mediated by myeloid cells releasing interleukin-10. Nat Commun (2022) 13(1):925. doi: 10.1038/s41586-018-0792-9

  • CrossRef
  • Google Scholar

• 113

  PuntSLangenhoffJMPutterHFleurenGJGorterAJordanovaES. The correlations between IL-17 vs. Th17 cells and cancer patient survival: a systematic review. Oncoimmunology (2015) 4(2):e984547. doi: 10.1038/s41586-021-03363-z

  • CrossRef
  • Google Scholar

• 114

  VedunovaMTurubanovaVVershininaOSavyukMEfimovaIMishchenkoTet al. DC vaccines loaded with glioma cells killed by photodynamic therapy induce Th17 anti-tumor immunity and provide a four-gene signature for glioma prognosis. Cell Death Dis (2022) 13(12):1062. doi: 10.1080/14760584.2022.2012456

  • CrossRef
  • Google Scholar

• 115

  GargADVandenberkLKoksCVerschuereTBoonLVan GoolSWet al. Dendritic cell vaccines based on immunogenic cell death elicit danger signals and T cell-driven rejection of high-grade glioma. Sci Transl Med (2016) 8(328):328ra27. doi: 10.1126/science.aaa4971

  • CrossRef
  • Google Scholar

• 116

  TomaszewskiWSanchez-PerezLGajewskiTFSampsonJH. Brain tumor microenvironment and host state: implications for immunotherapy. Clin Cancer Res (2019) 25(14):4202–10. doi: 10.1016/j.smim.2020.101385

  • CrossRef
  • Google Scholar

• 117

  CaiJHuYYeZYeLGaoLWangYet al. Immunogenic cell death-related risk signature predicts prognosis and characterizes the tumour microenvironment in lower-grade glioma. Front Immunol (2022) 13:1011757. doi: 10.1016/j.canlet.2022.215945

  • CrossRef
  • Google Scholar

• 118

  OchockaNSegitPWalentynowiczKAWojnickiKCyranowskiSSwatlerJet al. Single-cell RNA sequencing reveals functional heterogeneity of glioma-associated brain macrophages. Nat Commun (2021) 12(1):1151. doi: 10.1038/s41586-018-0810-y

  • CrossRef
  • Google Scholar

• 119

  RaviVMWillPKueckelhausJSunNJosephKSaliéHet al. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. Cancer Cell (2022) 40(6):639–55.e13. doi: 10.1016/j.addr.2022.114312

  • CrossRef
  • Google Scholar

• 120

  AbdelfattahNKumarPWangCLeuJSFlynnWFGaoRet al. Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target. Nat Commun (2022) 13(1):767. doi: 10.1038/s41467-022-28372-y

  • CrossRef
  • Google Scholar

• 121

  KryskoOLøve AaesTBachertCVandenabeelePKryskoDV. Many faces of DAMPs in cancer therapy. Cell Death Dis (2013) 4(5):e631. doi: 10.1136/jitc-2019-000329

  • CrossRef
  • Google Scholar

• 122

  KryskoDVAgostinisPKryskoOGargADBachertCLambrechtBNet al. Emerging role of damage-associated molecular patterns derived from mitochondria in inflammation. Trends Immunol (2011) 32(4):157–64. doi: 10.1158/2326-6066.CIR-15-0163

  • CrossRef
  • Google Scholar

• 123

  YangDPostnikovYVLiYTewaryPde la RosaGWeiFet al. High-mobility group nucleosome-binding protein 1 acts as an alarmin and is critical for lipopolysaccharide-induced immune responses. J Exp Med (2012) 209(1):157–71. doi: 10.1158/0008-5472.CAN-11-2957

  • CrossRef
  • Google Scholar

• 124

  FredlyHErsværEGjertsenBTBruserudO. Immunogenic apoptosis in human acute myeloid leukemia (AML): primary human AML cells expose calreticulin and release heat shock protein (HSP) 70 and HSP90 during apoptosis. Oncol Rep (2011) 25(6):1549–56. doi: 10.1126/scitranslmed.aat9143

  • CrossRef
  • Google Scholar

• 125

  BirmpilisAIPaschalisAMourkakisAChristodoulouPKostopoulosIVAntimissariEet al. Immunogenic cell death, DAMPs and prothymosin α as a putative anticancer immune response biomarker. Cells (2022) 11(9):1–21. doi: 10.1038/cddis.2013.156

  • CrossRef
  • Google Scholar

• 126

  GuXWuGYaoYZengJShiDLvTet al. Intratracheal administration of mitochondrial DNA directly provokes lung inflammation through the TLR9-p38 MAPK pathway. Free Radic Biol Med (2015) 83:149–58. doi: 10.1016/j.it.2011.01.005

  • CrossRef
  • Google Scholar

• 127

  VénéreauECeriottiCBianchiME. DAMPs from cell death to new life. Front Immunol (2015) 6:422. doi: 10.1084/jem.20101354

  • CrossRef
  • Google Scholar

• 128

  NakahiraKHisataSChoiAM. The roles of mitochondrial damage-associated molecular patterns in diseases. Antioxid Redox Signal (2015) 23(17):1329–50. doi: 10.1089/ars.2015.6407

  • CrossRef
  • Google Scholar

• 129

  SoriceMCircellaAMisasiRPittoniVGarofaloTCirelliAet al. Cardiolipin on the surface of apoptotic cells as a possible trigger for antiphospholipids antibodies. Clin Exp Immunol (2000) 122(2):277–84. doi: 10.3390/cells11091415

  • CrossRef
  • Google Scholar

• 130

  AdkinsISadilkovaLHradilovaNTomalaJKovarMSpisekR. Severe, but not mild heat-shock treatment induces immunogenic cell death in cancer cells. Oncoimmunology (2017) 6(5):e1311433. doi: 10.1016/j.freeradbiomed.2015.02.034

  • CrossRef
  • Google Scholar

• 131

  PodolskaMJShanXJankoCBoukherroubRGaiplUSSzuneritsSet al. Graphene-induced hyperthermia (GIHT) combined with radiotherapy fosters immunogenic cell death. Front Oncol (2021) 11:664615. doi: 10.3389/fonc.2021.664615

  • CrossRef
  • Google Scholar

• 132

  AdkinsIHradilovaNPalataOSadilkovaLPalova-JelinkovaLSpisekR. High hydrostatic pressure in cancer immunotherapy and biomedicine. Biotechnol Adv (2018) 36(3):577–82. doi: 10.1089/ars.2015.6407

  • CrossRef
  • Google Scholar

• 133

  SolariJIGFilippi-ChielaEPilarESNunesVGonzalezEAFigueiróFet al. Damage-associated molecular patterns (DAMPs) related to immunogenic cell death are differentially triggered by clinically relevant chemotherapeutics in lung adenocarcinoma cells. BMC Cancer. (2020) 20(1):474. doi: 10.1186/s12885-020-06964-5

  • CrossRef
  • Google Scholar

• 134

  ZhuMYangMZhangJYinYFanXZhangYet al. Immunogenic cell death induction by ionizing radiation. Front Immunol (2021) 12:705361. doi: 10.1080/2162402X.2017.1311433

  • CrossRef
  • Google Scholar

• 135

  MiyamotoSInoueHNakamuraTYamadaMSakamotoCUrataYet al. Coxsackievirus B3 is an oncolytic virus with immunostimulatory properties that is active against lung adenocarcinoma. Cancer Res (2012) 72(10):2609–21. doi: 10.3389/fonc.2021.664615

  • CrossRef
  • Google Scholar

• 136

  MaJRamachandranMJinCQuijano-RubioCMartikainenMYuDet al. Characterization of virus-mediated immunogenic cancer cell death and the consequences for oncolytic virus-based immunotherapy of cancer. Cell Death Dis (2020) 11(1):48. doi: 10.1016/j.biotechadv.2018.01.015

  • CrossRef
  • Google Scholar

• 137

  QinTXuXZhangZLiJYouXGuoHet al. Paclitaxel/sunitinib-loaded micelles promote an antitumor response in vitro through synergistic immunogenic cell death for triple-negative breast cancer. Nanotechnology (2020) 31(36):365101. doi: 10.1186/s12885-020-06964-5

  • CrossRef
  • Google Scholar

• 138

  MinuteLTeijeiraASanchez-PauleteAROchoaMCAlvarezMOtanoIet al. Cellular cytotoxicity is a form of immunogenic cell death. J Immunother Cancer (2020) 8(1):1–15. doi: 10.3389/fimmu.2021.705361

  • CrossRef
  • Google Scholar

• 139

  GalluzziLVitaleIWarrenSAdjemianSAgostinisPMartinezABet al. Consensus guidelines for the definition, detection and interpretation of immunogenic cell death. J Immunother Cancer (2020) 8(1):1–22. doi: 10.1158/0008-5472.CAN-11-3185

  • CrossRef
  • Google Scholar

• 140

  GoldszmidRSDzutsevATrinchieriG. Host immune response to infection and cancer: unexpected commonalities. Cell Host Microbe (2014) 15(3):295–305. doi: 10.1038/s41419-020-2236-3

  • CrossRef
  • Google Scholar

• 141

  CasaresNPequignotMOTesniereAGhiringhelliFRouxSChaputNet al. Caspase-dependent immunogenicity of doxorubicin-induced tumor cell death. J Exp Med (2005) 202(12):1691–701. doi: 10.1088/1361-6528/ab94dc

  • CrossRef
  • Google Scholar

• 142

  AaesTLKaczmarekADelvaeyeTDe CraeneBDe KokerSHeyndrickxLet al. Vaccination with necroptotic cancer cells induces efficient anti-tumor immunity. Cell Rep (2016) 15(2):274–87. doi: 10.1136/jitc-2019-000325

  • CrossRef
  • Google Scholar

• 143

  SprootenJDe WijngaertPVanmeerbeerkIMartinSVangheluwePSchlennerSet al. Necroptosis in immuno-oncology and cancer immunotherapy. Cells (2020) 9(8):1–30. doi: 10.1136/jitc-2019-000337corr1

  • CrossRef
  • Google Scholar

• 144

  EfimovaICatanzaroEvan der MeerenLTurubanovaVDHammadHMishchenkoTAet al. Vaccination with early ferroptotic cancer cells induces efficient antitumor immunity. J Immunother Cancer (2020) 8(2):1–15. doi: 10.1016/j.chom.2014.02.003

  • CrossRef
  • Google Scholar

• 145

  ZhengRRZhaoLPHuangCYChengHYangNChenZXet al. Paraptosis inducer to effectively trigger immunogenic cell death for metastatic tumor immunotherapy with IDO inhibition. ACS Nano. (2023) 17(11):9972–86. doi: 10.1084/jem.20050915

  • CrossRef
  • Google Scholar

• 146

  GalluzziLKeppOHettEKroemerGMarincolaFM. Immunogenic cell death in cancer: concept and therapeutic implications. J Transl Med (2023) 21(1):162. doi: 10.1016/j.celrep.2016.03.037

  • CrossRef
  • Google Scholar

• 147

  TurubanovaVDBalalaevaIVMishchenkoTACatanzaroEAlzeibakRPeskovaNNet al. Immunogenic cell death induced by a new photodynamic therapy based on photosens and photodithazine. J Immunother Cancer. (2019) 7(1):350. doi: 10.3390/cells9081823

  • CrossRef
  • Google Scholar

• 148

  TurubanovaVDMishchenkoTABalalaevaIVEfimovaIPeskovaNNKlapshinaLGet al. Novel porphyrazine-based photodynamic anti-cancer therapy induces immunogenic cell death. Sci Rep (2021) 11(1):7205. doi: 10.1136/jitc-2020-001369

  • CrossRef
  • Google Scholar

• 149

  MishchenkoTBalalaevaIGorokhovaAVedunovaMKryskoDV. Which cell death modality wins the contest for photodynamic therapy of cancer? Cell Death Dis (2022) 13(5):455. doi: 10.1021/acsnano.2c11964

  • CrossRef
  • Google Scholar

• 150

  ZhangDGaoMJinQNiYZhangJ. Updated developments on molecular imaging and therapeutic strategies directed against necrosis. Acta Pharm Sin B (2019) 9(3):455–68. doi: 10.1186/s12967-023-04017-6

  • CrossRef
  • Google Scholar

• 151

  KroemerGGalassiCZitvogelLGalluzziL. Immunogenic cell stress and death. Nat Immunol (2022) 23(4):487–500. doi: 10.1186/s40425-019-0826-3

  • CrossRef
  • Google Scholar

• 152

  MishchenkoTABalalaevaIVTurubanovaVDSaviukMOShylaginaNYu.et al. Gold standard assessment of immunogenic cell death induced by photodynamic therapy: From in vitro to tumor mouse models and anti-cancer vaccination strategies. In: Methods in Cell Biology. Amsterdam, Netherlands: Elsevier Inc (2023) 54:1–62.

  • Google Scholar

• 153

  HumeauJLévesqueSKroemerGPolJG. Gold standard assessment of immunogenic cell death in oncological mouse models. Methods Mol Biol (2019) 1884:297–315. doi: 10.1038/s41419-022-04851-4

  • CrossRef
  • Google Scholar

• 154

  HuJYeHZhangDLiuWLiMMaoYet al. U87MG glioma cells overexpressing IL-17 acclerate early-stage growth in vivo and cause a higher level of CD31 mRNA expression in tumor tissues. Oncol Lett (2013) 6(4):993–9. doi: 10.1016/j.apsb.2019.02.002

  • CrossRef
  • Google Scholar

• 155

  NumasakiMFukushiJOnoMNarulaSKZavodnyPJKudoTet al. Interleukin-17 promotes angiogenesis and tumor growth. Blood (2003) 101(7):2620–7. doi: 10.1038/s41590-022-01132-2

  • CrossRef
  • Google Scholar

• 156

  BenchetritFCireeAVivesVWarnierGGeyASautès-FridmanCet al. Interleukin-17 inhibits tumor cell growth by means of a T-cell-dependent mechanism. Blood (2002) 99(6):2114–21. doi: 10.1182/blood.v99.6.2114

  • CrossRef
  • Google Scholar

• 157

  BaileySRNelsonMHHimesRALiZMehrotraSPaulosCM. Th17 cells in cancer: the ultimate identity crisis. Front Immunol (2014) 5:276. doi: 10.1007/978-1-4939-8885-3_21

  • CrossRef
  • Google Scholar

• 158

  MadkouriRKaderbhaiCGBertautATruntzerCVincentJAubriot-LortonMHet al. Immune classifications with cytotoxic CD8(+) and Th17 infiltrates are predictors of clinical prognosis in glioblastoma. Oncoimmunology (2017) 6(6):e1321186. doi: 10.1038/nature08617

  • CrossRef
  • Google Scholar

• 159

  MitsdoerfferMAlyLBarzMEngleitnerTSieCDelbridgeCet al. The glioblastoma multiforme tumor site promotes the commitment of tumor-infiltrating lymphocytes to the T(H)17 lineage in humans. Proc Natl Acad Sci U S A. (2022) 119(34):e2206208119. doi: 10.1038/nature13387

  • CrossRef
  • Google Scholar

• 160

  MaYAymericLLocherCMattarolloSRDelahayeNFPereiraPet al. Contribution of IL-17-producing gamma delta T cells to the efficacy of anticancer chemotherapy. J Exp Med (2011) 208(3):491–503. doi: 10.1038/s41416-022-01864-w

  • CrossRef
  • Google Scholar

• 161

  ThorssonVGibbsDLBrownSDWolfDBortoneDSOu YangTHet al. The immune landscape of cancer. Immunity (2018) 48(4):812–30.e14. doi: 10.1007/s00401-016-1539-z

  • CrossRef
  • Google Scholar

• 162

  ZhouQYanXZhuHXinZZhaoJShenWet al. Identification of three tumor antigens and immune subtypes for mRNA vaccine development in diffuse glioma. Theranostics (2021) 11(20):9775–90. doi: 10.1007/s10014-020-00392-w

  • CrossRef
  • Google Scholar

• 163

  LinHWangKXiongYZhouLYangYChenSet al. Identification of tumor antigens and immune subtypes of glioblastoma for mRNA vaccine development. Front Immunol (2022) 13:773264. doi: 10.1007/s00401-019-02081-1

  • CrossRef
  • Google Scholar

• 164

  DangLWhiteDWGrossSBennettBDBittingerMADriggersEMet al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature (2009) 462(7274):739–44. doi: 10.1007/s00234-019-02269-y

  • CrossRef
  • Google Scholar

• 165

  SchumacherTBunseLPuschSSahmFWiestlerBQuandtJet al. A vaccine targeting mutant IDH1 induces antitumour immunity. Nature (2014) 512(7514):324–7. doi: 10.3174/ajnr.A6500

  • CrossRef
  • Google Scholar

• 166

  SchumacherTNSchreiberRD. Neoantigens in cancer immunotherapy. Science (2015) 348(6230):69–74. doi: 10.1186/s12883-020-01679-3

  • CrossRef
  • Google Scholar

• 167

  NejoTYamamichiAAlmeidaNDGoretskyYEOkadaH. Tumor antigens in glioma. Semin Immunol (2020) 47:101385. doi: 10.1007/s00401-015-1478-0

  • CrossRef
  • Google Scholar

• 168

  WangCYuMZhangW. Neoantigen discovery and applications in glioblastoma: An immunotherapy perspective. Cancer Lett (2022) 550:215945. doi: 10.1007/s00401-007-0243-4

  • CrossRef
  • Google Scholar

• 169

  HabashyKJMansourRMoussalemCSawayaRMassaadMJ. Challenges in glioblastoma immunotherapy: mechanisms of resistance and therapeutic approaches to overcome them. Br J Cancer. (2022) 127(6):976–87. doi: 10.1038/s41416-022-01864-w

  • CrossRef
  • Google Scholar

• 170

  HilfNKuttruff-CoquiSFrenzelKBukurVStevanovićSGouttefangeasCet al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature (2019) 565(7738):240–5. doi: 10.1038/s41586-018-0810-y

  • CrossRef
  • Google Scholar

• 171

  DunnGPSherpaNManyangaJJohannsTM. Considerations for personalized neoantigen vaccination in Malignant glioma. Adv Drug Delivery Rev (2022) 186:114312. doi: 10.1016/j.addr.2022.114312

  • CrossRef
  • Google Scholar

• 172

  VenteicherASTiroshIHebertCYizhakKNeftelCFilbinMGet al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science (2017) 355(6332):1–11. doi: 10.1007/s00401-019-01987-0

  • CrossRef
  • Google Scholar

• 173

  AmankulorNMKimYAroraSKarglJSzulzewskyFHankeMet al. Mutant IDH1 regulates the tumor-associated immune system in gliomas. Genes Dev (2017) 31(8):774–86. doi: 10.1186/s40478-020-00902-z

  • CrossRef
  • Google Scholar

• 174

  FujimotoKAritaHSatomiKYamasakiKMatsushitaYNakamuraTet al. TERT promoter mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic glioma with molecular features of glioblastoma. Acta Neuropathol. (2021) 142(2):323–38. doi: 10.1158/2159-8290.CD-19-1030

  • CrossRef
  • Google Scholar

• 175

  PanYHysingerJDBarronTSchindlerNFCobbOGuoXet al. NF1 mutation drives neuronal activity-dependent initiation of optic glioma. Nature (2021) 594(7862):277–82. doi: 10.1007/s10014-020-00378-8

  • CrossRef
  • Google Scholar

• 176

  LeyTJMardisERDingLFultonBMcLellanMDChenKet al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature (2008) 456(7218):66–72. doi: 10.1007/s00401-015-1493-1

  • CrossRef
  • Google Scholar

• 177

  SegalNHParsonsDWPeggsKSVelculescuVKinzlerKWVogelsteinBet al. Epitope landscape in breast and colorectal cancer. Cancer Res (2008) 68(3):889–92. doi: 10.1148/rg.2018180109

  • CrossRef
  • Google Scholar

• 178

  CastleJCKreiterSDiekmannJLöwerMvan de RoemerNde GraafJet al. Exploiting the mutanome for tumor vaccination. Cancer Res (2012) 72(5):1081–91. doi: 10.1007/s11060-021-03749-z

  • CrossRef
  • Google Scholar

• 179

  MatsushitaHVeselyMDKoboldtDCRickertCGUppaluriRMagriniVJet al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature (2012) 482(7385):400–4. doi: 10.1186/s40478-021-01222-6

  • CrossRef
  • Google Scholar

• 180

  JohannsTMWardJPMillerCAWilsonCKobayashiDKBenderDet al. Endogenous neoantigen-specific CD8 T cells identified in two glioblastoma models using a cancer immunogenomics approach. Cancer Immunol Res (2016) 4(12):1007–15. doi: 10.1371/journal.pone.0017948

  • CrossRef
  • Google Scholar

• 181

  KeskinDBAnandappaAJSunJTiroshIMathewsonNDLiSet al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature (2019) 565(7738):234–9. doi: 10.1038/s41586-018-0792-9

  • CrossRef
  • Google Scholar

• 182

  PlattenMBunseLWickABunseTLe CornetLHartingIet al. A vaccine targeting mutant IDH1 in newly diagnosed glioma. Nature (2021) 592(7854):463–8. doi: 10.1186/s12957-015-0603-9

  • CrossRef
  • Google Scholar

• 183

  RichardGPrinciottaMFBridonDMartinWDSteinbergGDDe GrootAS. Neoantigen-based personalized cancer vaccines: the emergence of precision cancer immunotherapy. Expert Rev Vaccines (2022) 21(2):173–84. doi: 10.1159/000028944

  • CrossRef
  • Google Scholar

• 184

  PatelGDavisCLiuYIpKDebideenKEAndersonSet al. Beyond-use date of trimix: A reproducible stability study using bracketing design. Int J Pharm Compd. (2021) 25(1):73–81. doi: 10.1148/rg.246045146

  • CrossRef
  • Google Scholar

• 185

  De KeersmaeckerBClaerhoutSCarrascoJBarICorthalsJWilgenhofSet al. TriMix and tumor antigen mRNA electroporated dendritic cell vaccination plus ipilimumab: link between T-cell activation and clinical responses in advanced melanoma. J Immunother Cancer (2020) 8(1):1–10. doi: 10.1097/00004728-199211000-00004

  • CrossRef
  • Google Scholar

• 186

  Van LintSRenmansDBroosKGoethalsLMaenhoutSBenteynDet al. Intratumoral delivery of triMix mRNA results in T-cell activation by cross-presenting dendritic cells. Cancer Immunol Res (2016) 4(2):146–56. doi: 10.1158/2326-6066.CIR-15-0163

  • CrossRef
  • Google Scholar

• 187

  Van LintSGoyvaertsCMaenhoutSGoethalsLDisyABenteynDet al. Preclinical evaluation of TriMix and antigen mRNA-based antitumor therapy. Cancer Res (2012) 72(7):1661–71. doi: 10.3389/fonc.2020.00502

  • CrossRef
  • Google Scholar

• 188

  HewittSLBaiABaileyDIchikawaKZielinskiJKarpRet al. Durable anticancer immunity from intratumoral administration of IL-23, IL-36γ, and OX40L mRNAs. Sci Transl Med (2019) 11(477):1–15. doi: 10.25259/SNI_1208_2021

  • CrossRef
  • Google Scholar