Ricardo Gargini

Chimeric antigen receptor (CAR) T-cell therapy has reshaped the treatment of hematologic malignancies, leading to remarkable responses in diseases that were once considered largely refractory. However, its translation to solid tumors, and particularly to glioblastoma (GBM), has remained far more limited. GBM continues to be one of the most aggressive and treatment-resistant brain tumors, characterized by profound intratumoral heterogeneity, restricted immune cell trafficking, and a highly immunosuppressive tumor microenvironment. These features collectively constrain the efficacy of CAR-T cells within the central nervous system (CNS). In this review, we propose a conceptual framework in which CAR-T-associated neurotoxicity is not interpreted solely as a treatment-induced adverse event but rather as the consequence of immune activation occurring within a preconditioned neuroinflammatory landscape. We propose that blood-brain barrier (BBB) disruption, myeloid-driven inflammation, vascular dysfunction, and intracranial spatial constraints collectively establish a vulnerable CNS state that amplifies CAR-T-induced inflammatory responses. In this regard, cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and tumor inflammation-associated neurotoxicity (TIAN) can be understood as interconnected and spatially modulated manifestations of a shared pathophysiological process. By integrating these processes, we provide a unified framework that links CNS-specific biology with immune-mediated toxicity across different clinical settings. This integrative perspective provides a mechanistic framework for interpreting neurotoxicity in GBM and may inform the development of safer and more effective CAR-T cell therapies. This highlights the importance of developing context-adapted CAR-T and combination strategies that account for the pre-existing tumor microenvironment and its associated baseline neuroinflammatory state, as key determinants of treatment-related toxicity.

Glioblastoma (GBM) is one of the deadliest cancers, largely due to its nearly inevitable recurrence driven by residual tumor cells. Recent evidence suggests that performing supratotal resections that extend into non‐contrast‐enhancing (nCE) tumor regions may prolong survival. However, tumor infiltration in these areas has not been fully characterized. Our goal was to define the cellular landscape of the GBM periphery and its clinical relevance. To accomplish this, we prospectively collected 161 biopsies from 45 GBM patients. The samples were classified using magnetic resonance imaging (MRI) as either enhancing tumor, nCE tumor, vasogenic edema, or radiologically normal tissue. We performed immunohistochemistry with MIB1 staining to detect proliferative cells and measure cell density. We also performed transcriptional profiling, selecting immune, glial, and vascular markers from single‐cell RNA sequencing data. We analyzed the associations with the extent of resection and patient outcomes. Most nCE tumor biopsies displayed neoplastic features, including high cellularity, proliferative markers, and loss of oligodendrocyte signatures. However, some regions labeled as edema by MRI also contained infiltrating tumor cells and were associated with disease progression. Therefore, although MRI‐based classification of nCE tumor regions is generally accurate, it underestimates infiltration within edema‐labeled areas. Furthermore, in patients who underwent gross‐total resection, high peripheral cellularity predicted earlier local recurrence. This suggests that assessing cell density in the periphery can improve recurrence risk prediction. Regarding the tumor microenvironment analysis, immune‐related phenotypes were associated with a worse prognosis only after complete resection of the enhancing tumor. Furthermore, our gene panel, particularly the oligodendrocyte markers, provides prognostic insights depending on both biopsy location and resection extent. These findings support the integration of histological and molecular evaluations of the tumor periphery into surgical and therapeutic decision‐making processes. This approach offers a path to better patient stratification and outcome prediction in GBM.

The connection between aging and immune dysfunction is uncovering how immunoaging processes contribute to disease. Recent data from Alzheimer’s disease, Parkinson’s disease, and adult and pediatric glioblastomas reveal a novel role for TIM3 in brain immune alteration. These findings highlight the role of TIM3 in promoting myeloid cell dysfunction toward an immunosuppressive profile. TIM3-blocking antibody treatments for central nervous system pathologies could be a new therapeutic window for healthcare systems.

Hyperspectral imaging (HSI) extends conventional histopathology by combining spatial morphology with rich spectral information that reflects tissue biochemical composition, offering new opportunities for quantitative tissue analysis. However, reliable spectral analysis requires accurate instance-level segmentation of cell nuclei to enable the construction of meaningful nuclear spectral databases. In this work, a comprehensive methodology for generating hyperspectral databases of cell nuclei from histopathological samples is presented, including hyperspectral acquisition, preprocessing, nucleus segmentation, and spectral signature extraction. Three nucleus segmentation methods are evaluated: a spectral-only approach based on pixel-wise hyperspectral signatures in the visible–VNIR range; a spatial-only approach using synthetic RGB images derived from hyperspectral cubes; and a combined spatial–spectral approach that jointly exploits spatial and spectral information. The methods are assessed on a proprietary dataset of 30 hyperspectral cubes of tumor and healthy histopathological brain tissue annotated by expert pathologists. The spectral-only method achieves a Dice similarity coefficient (DSC) of 61.89% and produces severe over-segmentation, with cell count deviations exceeding substantially the ground truth in healthy tissue. The spatial-only method attains the highest pixel-wise accuracy (78.97% DSC) but underestimates nucleus counts by approximately 30% in tumor regions due to nucleus merging. The spatial–spectral method achieves a DSC of 73.13% and a mean cell count deviation of 4%, providing more reliable instance-level separation. These findings demonstrate that pixel-wise accuracy alone is insufficient for hyperspectral nuclear database generation.

Glioblastoma (GBM) exhibits a high recurrence rate of 95% due to its highly infiltrative nature and marked heterogeneity, making its diagnosis challenging. These characteristics complicate the standardization of diagnostic criteria and contribute to significant interpathologist variability. In response, artificial intelligence-based tools are increasingly being developed to support the diagnostic process. This study presents a complete processing pipeline that integrates deep learning and machine learning techniques to facilitate the screening of hematoxylin and eosin-stained histopathological images, aiming to reduce the workload in pathology departments. The proposed workflow includes a convolutional neural network capable of detecting and segmenting individual cells, achieving 89.5% pixel-wise precision and a DICE coefficient (DSC) of 88.4%, indicating a high degree of spatial overlap between the predicted and reference regions. Following segmentation, a set of morphological features is extracted, including cell area, circularity, cell count, and the average distance between neighboring cells. The extracted features were subsequently used for tissue classification into tumorous and non-tumorous categories via a random forest algorithm, resulting in a high classification accuracy of 94.15%, thereby suggesting the robustness and reliability of the proposed approach. The applied methodology was validated on a cohort of 82 patients, comprising both tumor and healthy tissue, with a total of 1557 images and more than 4 million annotated cells.

In 2016, the World Health Organization (WHO) classification introduced molecular criteria [isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion] for the diagnosis of oligodendroglioma. The cyclin pathway (cyclin-dependent kinase inhibitor 2A deletion/p16 expression) has been extensively studied in this tumour but was performed in series based only on histopathological criteria. This study analysed this pathway in oligodendrogliomas with isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion. Cases with morphological diagnosis of oligodendroglioma were identified (182 cases). This same cohort was subsequently reclassified according to the current 2021 World Health Organization (WHO) (91 cases). The type of isocitrate dehydrogenase (IDH) (determined by high-resolution melting and immunohistochemistry) and telomerase reverse transcriptase (TERT) mutations were analysed. Also, p16, pRb and cyclin D1 were studied by immunohistochemistry. In the reclassified cohort, 82 cases (90.1%) had isocitrate dehydrogenase1 (IDH1) mutation and 9 cases (9.9%) had isocitrate dehydrogenase 2 (IDH2) mutation. Eighty cases (87.9%) had TERT mutation, 33 cases (36.3%) had 250T, and 47 cases (51.6%) had C228T mutation. 16 of 71 cases (22.5%) had no p16 expression, and was significantly associated with a worse prognosis; the median OS in the absence of p16 was 9 years (95% CI 9.36–17.35) versus 13.35 years with p16 expression (95% CI 9.36–17.35) P = 0.023 HR 0.41. With a clear difference in Grade 3 (4.76 versus 17.49 years). In a multivariate analysis, only the absence of p16 showed a statistically significant prognostic value (P = 0.034). In Grade 2 oligodendrogliomas, high cyclin D1 expression was associated with worse survival. In this cohort of oligodendroglial tumours classified by molecular criteria, the loss of p16 expression is associated with a poor prognosis, particularly in Grade 3 oligodendrogliomas.

Supplementary figure S3. Characterization of TRIB2 expression in murine glioma models.

The lack of response of glioblastoma (GBM) to immunotherapy is closely related to the limited number of T cells in the tumor microenvironment. However, it is still not known why GBM is characterized by an immune-cold tumor microenvironment with reduced CD8+ T-cell infiltration when there is substantial myeloid cell infiltration and a substantial alteration of the blood-brain barrier. The aim of this study was to identify regulators of low CD8+ T-cell infiltration in GBM. Using transcriptomic screening, we found that tribbles homolog 2 (TRIB2) is a regulator of the immune-cold microenvironment characteristic of GBM. Further analysis of a cohort of 114 brain tumors with IHC, RNA sequencing, and qRT-PCR showed that TRIB2 inhibited the transcription of genes involved in antigen presentation by the tumor cells and those involved in T-cell recruitment by modulating the expression of methylation regulators, in particular DNA methyltransferase 1. Further, we observed 75% survival after TRIB2 inhibition in murine glioma models and showed transcriptomic reprogramming by decitabine of genes involved in the processes described above. In our patient-derived tumor fragments assay, we observed a consistent, generalized response to decitabine, suggesting that DNA methyltransferase 1 inhibition (DNMT1) could be a promising therapeutic strategy for GBM.