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Gliomas synaptically integrate into neural circuits 1 , 2 . Previous research has demonstrated bidirectional interactions between neurons and glioma cells, with neuronal activity driving glioma growth 1 – 4 and gliomas increasing neuronal excitability 2 , 5 – 8 . Here we sought to determine how glioma-induced neuronal changes influence neural circuits underlying cognition and whether these interactions influence patient survival. Using intracranial brain recordings during lexical retrieval language tasks in awake humans together with site-specific tumour tissue biopsies and cell biology experiments, we find that gliomas remodel functional neural circuitry such that task-relevant neural responses activate tumour-infiltrated cortex well beyond the cortical regions that are normally recruited in the healthy brain. Site-directed biopsies from regions within the tumour that exhibit high functional connectivity between the tumour and the rest of the brain are enriched for a glioblastoma subpopulation that exhibits a distinct synaptogenic and neuronotrophic phenotype. Tumour cells from functionally connected regions secrete the synaptogenic factor thrombospondin-1, which contributes to the differential neuron–glioma interactions observed in functionally connected tumour regions compared with tumour regions with less functional connectivity. Pharmacological inhibition of thrombospondin-1 using the FDA-approved drug gabapentin decreases glioblastoma proliferation. The degree of functional connectivity between glioblastoma and the normal brain negatively affects both patient survival and performance in language tasks. These data demonstrate that high-grade gliomas functionally remodel neural circuits in the human brain, which both promotes tumour progression and impairs cognition. High-grade gliomas functionally remodel neural circuits in the human brain, promoting tumour progression and impairing cognition.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery 1 . The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive 2 , 3 . Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce 4 . In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH) 5 – 7 , a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20–30 min) 2 . In a multicenter, prospective clinical trial ( n  = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory. A prospective, multicenter, case–control clinical trial evaluates the potential of artificial intelligence for providing accurate bedside diagnosis of patients with brain tumors.

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma ( n  = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3 ± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma. DeepGlioma, a multimodal deep learning approach for intraoperative diagnostic screening of diffuse glioma, trained on stimulated Raman histology and large-scale public genomic data, can predict molecular alterations for diffuse glioma diagnosis with high accuracy.

Glioma resection is associated with prolonged survival, but neuro-oncological trials have frequently refrained from quantifying the extent of resection. The Response Assessment in Neuro-Oncology (RANO) resect group is an international, multidisciplinary group that aims to standardise research practice by delineating the oncological role of surgery in diffuse adult-type gliomas as defined per WHO 2021 classification. Favourable survival effects of more extensive resection unfold over months to decades depending on the molecular tumour profile. In tumours with a more aggressive natural history, supramaximal resection might correlate with additional survival benefit. Weighing the expected survival benefits of resection as dictated by molecular tumour profiles against clinical factors, including the introduction of neurological deficits, we propose an algorithm to estimate the oncological effects of surgery for newly diagnosed gliomas. The algorithm serves to select patients who might benefit most from extensive resection and to emphasise the relevance of quantifying the extent of resection in clinical trials.

AbstractIntrinsic brain tumors often occur within functional neural networks, leading to neurological impairment and disability of varying degrees. Advances in our understanding of tumor-network integration, human cognition and language processing, and multiparametric imaging, combined with refined intraoperative tumor resection techniques, have enhanced surgical management of intrinsic brain tumors within eloquent areas. However, cognitive symptoms impacting health-related quality of life, particularly processing speed, attention, concentration, working memory, and executive function, often persist after the postoperative recovery period and treatment. Multidisciplinary cognitive rehabilitation is the standard of care for addressing cognitive impairments in many neurological diseases. There is promising research to support the use of cognitive rehabilitation in adult brain tumor patients. In this review, we summarize the history and usefulness of postacute cognitive rehabilitation for adult brain tumor patients.

IntroductionMultiple studies have demonstrated that improved extent of resection is associated with longer overall survival for patients with both high and low grade glioma. Awake craniotomy was developed as a technique for maximizing resection whilst preserving neurological function.MethodsWe performed a comprehensive review of the literature describing the history, indications, techniques and outcomes of awake craniotomy for patients with glioma.ResultsThe technique of awake craniotomy evolved to become an essential tool for resection of glioma. Many perceived contraindications can now be managed. We describe in detail our preferred technique, the testing paradigms utilized, and critically review the literature regarding functional and oncological outcome.ConclusionsAwake craniotomy with mapping has become the gold standard for safely maximizing extent of resection for tumor in or near eloquent brain. Cortical and subcortical mapping methods have been refined and the technique is associated with an extremely low rate of complications.

Abstract Intraoperative language mapping of tumor and peritumor tissue is a well-established technique for avoiding permanent neurological deficits and maximizing extent of resection. Although there are several components of language that may be tested intraoperatively (eg, naming, writing, reading, and repetition), there is a lack of consistency in how patients are tested intraoperatively as well as the techniques involved to ensure safety during an awake procedure. Here, we review appropriate patient selection, neuroanesthetic techniques, cortical and subcortical language mapping stimulation paradigms, and selection of intraoperative language tasks used during awake craniotomies. We also expand on existing language mapping reviews by considering how intensity and timing of electrical stimulation may impact interpretation of mapping results.

In the management of diffuse gliomas, the identification and removal of tumor at the infiltrative margin remains a central challenge. Prior work has demonstrated that fluorescence labeling tools and radiographic imaging are useful surgical adjuvants with macroscopic resolution. However, they lose sensitivity at the tumor margin and have limited clinical utility for lower grade histologies. Fiber-laser based stimulated Raman histology (SRH) is an optical imaging technique that provides microscopic tissue characterization of unprocessed tissues. It remains unknown whether SRH of tissues taken from the infiltrative glioma margin will identify microscopic residual disease. Here we acquired glioma margin specimens for SRH, histology, and tumor specific tissue characterization. Generalized linear mixed models were used to evaluate agreement. We find that SRH identified residual tumor in 82 of 167 margin specimens (49%), compared to IHC confirming residual tumor in 72 of 128 samples (56%), and H&E confirming residual tumor in 82 of 169 samples (49%). Intraobserver agreements between all 3 modalities were confirmed. These data demonstrate that SRH detects residual microscopic tumor at the infiltrative glioma margin and may be a promising tool to enhance extent of resection.

BACKGROUNDLong-term prognosis of WHO grade II low-grade gliomas (LGGs) is poor, with a high risk of recurrence and malignant transformation into high-grade gliomas. Given the relatively intact immune system of patients with LGGs and the slow tumor growth rate, vaccines are an attractive treatment strategy.METHODSWe conducted a pilot study to evaluate the safety and immunological effects of vaccination with GBM6-AD, lysate of an allogeneic glioblastoma stem cell line, with poly-ICLC in patients with LGGs. Patients were randomized to receive the vaccines before surgery (arm 1) or not (arm 2) and all patients received adjuvant vaccines. Coprimary outcomes were to evaluate safety and immune response in the tumor.RESULTSA total of 17 eligible patients were enrolled - 9 in arm 1 and 8 in arm 2. This regimen was well tolerated with no regimen-limiting toxicity. Neoadjuvant vaccination induced upregulation of type-1 cytokines and chemokines and increased activated CD8+ T cells in peripheral blood. Single-cell RNA/T cell receptor sequencing detected CD8+ T cell clones that expanded with effector phenotype and migrated into the tumor microenvironment (TME) in response to neoadjuvant vaccination. Mass cytometric analyses detected increased tissue resident-like CD8+ T cells with effector memory phenotype in the TME after the neoadjuvant vaccination.CONCLUSIONThe regimen induced effector CD8+ T cell response in peripheral blood and enabled vaccine-reactive CD8+ T cells to migrate into the TME. Further refinements of the regimen may have to be integrated into future strategies.TRIAL REGISTRATIONClinicalTrials.gov NCT02549833.FUNDINGNIH (1R35NS105068, 1R21CA233856), Dabbiere Foundation, Parker Institute for Cancer Immunotherapy, and Daiichi Sankyo Foundation of Life Science.

Neuronal activity-driven mechanisms influence glioblastoma cell proliferation and invasion, while glioblastoma remodels neuronal circuits. Although a subpopulation of malignant cells enhances neuronal connectivity, their impact on the immune system remains unclear. Here, we show that glioblastoma regions with enhanced neuronal connectivity exhibit regional immunosuppression, characterized by distinct immune cell compositions and the enrichment of anti-inflammatory tumor-associated macrophages (TAMs). In preclinical models, knockout of Thrombospondin-1 (TSP1/ Thbs1 ) in glioblastoma cells suppresses synaptogenesis and glutamatergic neuronal hyperexcitability. Furthermore, TSP1 knockout restores antigen presentation-related genes, promotes the infiltration of pro-inflammatory TAMs and CD8 + T-cells in the tumor, and alleviates TAM-mediated T-cell suppression. Pharmacological inhibition of glutamatergic signaling also shifts TAMs toward a less immunosuppressive state, prolongs survival in mice, and shows the potential to enhance the efficacy of immune cell-based therapy. These findings confirm that glioma-neuronal circuit remodeling is strongly linked with regional immunosuppression and suggest that targeting glioma-neuron-immune crosstalk could provide avenues for immunotherapy. Immunotherapy has yet to demonstrate efficacy for patients with glioblastoma. Here, the authors employ human single-cell RNA-seq, spatial transcriptomics, and a preclinical mouse model to show that glioblastoma cell-derived synaptogenic factor Thrombospondin-1 promotes neuronal circuit remodeling and regional immunosuppression, highlighting a potential therapeutic target.