Hallmarks of the Tumour Microenvironment of Gliomas and Its Interaction with Emerging Immunotherapy Modalities.

Gliomas are aggressive, primary central nervous system tumours arising from glial cells. Glioblastomas are the most malignant. They are known for their poor prognosis or median overall survival. The current standard of care is overwhelmed by the heterogeneous, immunosuppressive tumour microenvironment promoting immune evasion and tumour proliferation. The advent of immunotherapy with its various modalities-immune checkpoint inhibitors, cancer vaccines, oncolytic viruses and chimeric antigen receptor T cells and NK cells-has shown promise. Clinical trials incorporating combination immunotherapies have overcome the microenvironment resistance and yielded promising survival and prognostic benefits. Rolling these new therapies out in the real-world scenario in a low-cost, high-throughput manner is the unmet need of the hour. These will have practice-changing implications to the glioma treatment landscape. Here, we review the immunobiological hallmarks of the TME of gliomas, how the TME evades immunotherapies and the work that is being conducted to overcome this interplay.

Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study).

BACKGROUND: The aim was to predict survival of glioblastoma at 8 months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. METHODS: Retrospective and prospective data were collected from 206 consecutive glioblastoma, isocitrate dehydrogenase -wildtype patients diagnosed between March 2014 and February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from 3 centers. Holdout test sets were retrospective (n = 19; internal validation), and prospective (n = 29; external validation from 8 distinct centers). Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A nonimaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; nonimaging features; and weighted dense blocks pretrained for abnormality detection. RESULTS: The imaging model outperformed the nonimaging model in all test sets (area under the receiver-operating characteristic curve, AUC P = .038) and performed similarly to a combined imaging/nonimaging model (P > .05). Imaging, nonimaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10 000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; P = .003). CONCLUSIONS: A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Lemierre's syndrome involving milleri group streptococci: further insight into age and aetiology.

Lemierre's syndrome (LS) is a suppurative thrombophlebitis of the internal jugular vein secondary to otorhinolaryngologic infection. It is classically associated with the Gram-negative anaerobe Fusobacterium necrophorum (FN) and is thought to be a disease of young people. Here, we describe the case of a 56-year-old woman with LS involving milleri group streptococci (MGS), which has been reported only 13 times since it was first observed in 2003. Subgroup analysis of all published cases of LS involving MGS demonstrated these patients were significantly older than those involving FN (median age 49 years versus 18 years, p = 0.007, IQR 36-58 years), although this finding is limited by publication bias. This report clarifies a 2014 hypothesis regarding the relationship between age and aetiology in this rare disease. While FN remains the most common cause of LS overall, empiric antibiotic therapy should also cover oral streptococci such as MGS, even in younger adults.