Serum beta2-microglobulin acts as a biomarker for severity and prognosis in glioma patients: a preliminary clinical study.

BACKGROUND: Gliomas are the deadliest malignant tumors of the adult central nervous system. We previously discovered that beta2-microglobulin (B2M) is abnormally upregulated in glioma tissues and that it exerts a range of oncogenic effects. Besides its tissue presence, serum B2M levels serve as biomarkers for various diseases. This study aimed to explore whether serum B2M levels can be used in the diagnosis and prognosis of gliomas. METHODS: Medical records from 246 glioma patients were retrospectively analyzed. The relationship between preoperative serum B2M levels and clinicopathological features was examined. Kaplan-Meier analysis, alongside uni- and multivariate Cox regression, assessed the association between B2M levels, systemic inflammatory markers, and glioma patient prognosis. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic significance of these biomarkers specifically for glioblastoma (GBM). RESULTS: Patients with malignant gliomas exhibited elevated preoperative serum B2M levels. Glioma patients with high serum B2M levels experienced shorter survival times. Multivariate Cox analysis determined the relationship between B2M levels (hazard ratio = 1.92, 95% confidence interval: 1.05-3.50, P = 0.034) and the overall survival of glioma patients. B2M demonstrated superior discriminatory power in distinguishing between GBM and non-GBM compared to inflammation indicators. Moreover, postoperative serum B2M levels were lower than preoperative levels in the majority of glioma patients. CONCLUSIONS: High preoperative serum B2M levels correlated with malignant glioma and a poor prognosis. Serum B2M shows promise as a novel biomarker for predicting patient prognosis and reflecting the therapeutic response.

Improving medical students recognizing surgery of glioblastoma removal/decompressive craniectomy via physical lifelike brain simulator training.

BACKGROUND: This study aims to investigate the benefits of employing a Physical Lifelike Brain (PLB) simulator for training medical students in performing craniotomy for glioblastoma removal and decompressive craniectomy. METHODS: This prospective study included 30 medical clerks (fifth and sixth years in medical school) at a medical university. Before participating in the innovative lesson, all students had completed a standard gross anatomy course as part of their curriculum. The innovative lesson involved PLB Simulator training, after which participants completed the Learning Satisfaction/Confidence Perception Questionnaire and some received qualitative interviews. RESULTS: The average score of students' overall satisfaction with the innovative lesson was 4.71 out of a maximum of 5 (SD = 0.34). After the lesson, students' confidence perception level improved significantly (t = 9.38, p < 0.001, effect size = 1.48), and the average score improved from 2,15 (SD = 1.02) to 3.59 (SD = 0.93). 60% of the students thought that the innovative lesson extremely helped them understand the knowledge of surgical neuroanatomy more, 70% believed it extremely helped them improve their skills in burr hole, and 63% thought it was extremely helpful in improving the patient complications of craniotomy with the removal of glioblastoma and decompressive craniectomy after completing the gross anatomy course. CONCLUSION: This innovative lesson with the PLB simulator successfully improved students' craniotomy knowledge and skills.

The tumour microenvironment, treatment resistance and recurrence in glioblastoma.

The adaptability of glioblastoma (GBM) cells, encouraged by complex interactions with the tumour microenvironment (TME), currently renders GBM an incurable cancer. Despite intensive research, with many clinical trials, GBM patients rely on standard treatments including surgery followed by radiation and chemotherapy, which have been observed to induce a more aggressive phenotype in recurrent tumours. This failure to improve treatments is undoubtedly a result of insufficient models which fail to incorporate components of the human brain TME. Research has increasingly uncovered mechanisms of tumour-TME interactions that correlate to worsened patient prognoses, including tumour-associated astrocyte mitochondrial transfer, neuronal circuit remodelling and immunosuppression. This tumour hijacked TME is highly implicated in driving therapy resistance, with further alterations within the TME and tumour resulting from therapy exposure inducing increased tumour growth and invasion. Recent developments improving organoid models, including aspects of the TME, are paving an exciting future for the research and drug development for GBM, with the hopes of improving patient survival growing closer. This review focuses on GBMs interactions with the TME and their effect on tumour pathology and treatment efficiency, with a look at challenges GBM models face in sufficiently recapitulating this complex and highly adaptive cancer.

The clinical, molecular, and therapeutic implications of time from primary diagnosis to brain metastasis in lung and breast cancer patients.

PURPOSE: Lung cancer (LC) and breast cancer (BC) are the most common causes of brain metastases (BMs). Time from primary diagnosis to BM (TPDBM) refers to the time interval between initial LC or BC diagnosis and development of BM. This research aims to identify clinical, molecular, and therapeutic risk factors associated with shorter TPDBM. METHODS: We retrospectively reviewed all diagnosed LC and BC patients with BM at Harbin Medical University Cancer Hospital from 2016 to 2020. A total of 570 patients with LC brain metastasis (LCBM) and 173 patients with breast cancer brain metastasis (BCBM) patients who met the inclusion criteria were enrolled for further analysis. BM free survival time curves were generated using Kaplan-Meier analyses. Univariate and multivariate Cox regression analyses were applied to identify risk factors associated with earlier development of BM in LC and BC, respectively. RESULTS: The median TPDBM was 5.3 months in LC and 44.4 months in BC. In multivariate analysis, clinical stage IV and M1 stage were independent risk factors for early development of LCBM. LC patients who received chemotherapy, targeted therapy, pulmonary radiotherapy, and pulmonary surgery had longer TPDBM. For BC patients, age ≥ 50 years, Ki67 ≥ 0.3, HER2 positive or triple-negative breast cancer subtype, advanced N stage, and no mastectomy were correlated with shorter TPDBM. CONCLUSIONS: This single-institutional study helps identify patients who have a high risk of developing BM early. For these patients, early detection and intervention could have clinical benefits.

Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma.

Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time-dependent ROC and calibration plots. In addition, a high-risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD-L1 and vimentin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.

The interplay mechanism between IDH mutation, MGMT-promoter methylation, and PRMT5 activity in the progression of grade 4 astrocytoma: unraveling the complex triad theory.

Background: The dysregulation of Isocitrate dehydrogenase (IDH) and the subsequent production of 2-Hydroxyglutrate (2HG) may alter the expression of epigenetic proteins in Grade 4 astrocytoma. The interplay mechanism between IDH, O-6-methylguanine-DNA methyltransferase (MGMT)-promoter methylation, and protein methyltransferase proteins-5 (PRMT5) activity, with tumor progression has never been described. Methods: A retrospective cohort of 34 patients with G4 astrocytoma is classified into IDH-mutant and IDH-wildtype tumors. Both groups were tested for MGMT-promoter methylation and PRMT5 through methylation-specific and gene expression PCR analysis. Inter-cohort statistical significance was evaluated. Results: Both IDH-mutant WHO grade 4 astrocytomas (n = 22, 64.7%) and IDH-wildtype glioblastomas (n = 12, 35.3%) had upregulated PRMT5 gene expression except in one case. Out of the 22 IDH-mutant tumors, 10 (45.5%) tumors showed MGMT-promoter methylation and 12 (54.5%) tumors had unmethylated MGMT. All IDH-wildtype tumors had unmethylated MGMT. There was a statistically significant relationship between MGMT-promoter methylation and IDH in G4 astrocytoma (p-value = 0.006). Statistically significant differences in progression-free survival (PFS) were also observed among all G4 astrocytomas that expressed PRMT5 and received either temozolomide (TMZ) or TMZ plus other chemotherapies, regardless of their IDH or MGMT-methylation status (p-value=0.0014). Specifically, IDH-mutant tumors that had upregulated PRMT5 activity and MGMT-promoter methylation, who received only TMZ, have exhibited longer PFS. Conclusions: The relationship between PRMT5, MGMT-promoter, and IDH is not tri-directional. However, accumulation of D2-hydroxyglutarate (2-HG), which partially activates 2-OG-dependent deoxygenase, may not affect their activities. In IDH-wildtype glioblastomas, the 2HG-2OG pathway is typically inactive, leading to PRMT5 upregulation. TMZ alone, compared to TMZ-plus, can increase PFS in upregulated PRMT5 tumors. Thus, using a PRMT5 inhibitor in G4 astrocytomas may help in tumor regression.

A Computational Framework for the Administration of 5-Aminovulinic Acid Before Glioblastoma Surgery.

5-Aminolevulinic Acid (5-ALA) is the only fluorophore approved by the FDA as an intraoperative optical imaging agent for fluorescence-guided surgery in patients with glioblastoma. The dosing regimen is based on rodent tests where a maximum signal occurs around 6 h after drug administration. Here, we construct a computational framework to simulate the transport of 5-ALA through the stomach, blood, and brain, and the subsequent conversion to the fluorescent agent protoporphyrin IX at the tumor site. The framework combines compartmental models with spatially-resolved partial differential equations, enabling one to address questions regarding quantity and timing of 5-ALA administration before surgery. Numerical tests in two spatial dimensions indicate that, for tumors exceeding the detection threshold, the time to peak fluorescent concentration is 2-7 h, broadly consistent with the current surgical guidelines. Moreover, the framework enables one to examine the specific effects of tumor size and location on the required dose and timing of 5-ALA administration before glioblastoma surgery.

A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis.

Aiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and Radial Basis Probabilistic Neural Network (RBPNN) is proposed model. Taking ResNet50 as a visual modeling network, it uses feature pyramid and self-attention mechanism to extract appearance and semantic features of images at multiple scales, and associate and enhance local and global features. Taking into account the diversity of category features, channel cosine similarity attention and dynamic C-means clustering algorithms are used to select representative sample features in different category of sample subsets to implicitly express prior category feature knowledge, and use them as the kernel centers of radial basis probability neurons (RBPN) to realize the embedding of diverse prior feature knowledge. In the RBPNN pattern aggregation layer, the outputs of RBPN are selectively summed according to the category of the kernel center, that is, the subcategory features are combined into category features, and finally the image classification is implemented based on Softmax. The functional module of the proposed method is designed specifically for image characteristics, which can highlight the significance of local and structural features of the image, form a non-convex decision-making area, and reduce the requirements for the completeness of the sample set. Applying the proposed method to medical image classification, experiments were conducted based on the brain tumor MRI image classification public dataset and the actual cardiac ultrasound image dataset, and the accuracy rate reached 85.82% and 83.92% respectively. Compared with the three mainstream image classification models, the performance indicators of this method have been significantly improved.

Endoplasmic reticulum stress-dependent regulation of the expression of serine hydroxymethyltransferase 2 in glioblastoma cells.

Objective. Serine hydroxymethyltransferase (SHMT2) plays a multifunctional role in mitochondria (folate-dependent tRNA methylation, translation, and thymidylate synthesis). The endoplasmic reticulum stress, hypoxia, and glucose and glutamine supply are significant factors of malignant tumor growth including glioblastoma. Previous studies have shown that the knockdown of the endoplasmic reticulum to nucleus signaling 1 (ERN1) pathway of endoplasmic reticulum stress strongly suppressed glioblastoma cell proliferation and modified the sensitivity of these cells to hypoxia and glucose or glutamine deprivations. The present study aimed to investigate the regulation of the SHMT2 gene in U87MG glioblastoma cells by ERN1 knockdown, hypoxia, and glucose or glutamine deprivations with the intent to reveal the role of ERN1 signaling in sensitivity of this gene expression to hypoxia and nutrient supply. Methods. The control U87MG glioblastoma cells (transfected by an empty vector) and ERN1 knockdown cells with inhibited ERN1 endoribonuclease and protein kinase (dnERN1) or only ERN1 endoribonuclease (dnrERN1) were used. Hypoxia was introduced by dimethyloxalylglycine (500 ng/ml for 4 h). For glucose and glutamine deprivations, cells were exposed in DMEM without glucose and glutamine, respectively for 16 h. RNA was extracted from cells and reverse transcribed. The expression level of the SHMT2 gene was studied by real-time qPCR and normalized to ACTB. Results. It was found that inhibition of ERN1 endoribonuclease and protein kinase in glioblastoma cells led to a down-regulation of SHMT2 gene expression in U87MG cells. At the same time, the expression of this gene did not significantly change in cells with inhibited ERN1 endoribonuclease, but tunicamycin strongly increased its expression. Moreover, the expression of the SHMT2 gene was not affected in U87MG cells after silencing of XBP1. Hypoxia up-regulated the expression level of the SHMT2 gene in both control and ERN1 knockdown U87MG cells. The expression of this gene was significantly up-regulated in glioblastoma cells under glucose and glutamine deprivations and ERN1 knockdown significantly increased the sensitivity of the SHMT2 gene to these nutrient deprivation conditions. Conclusion. The results of the present study demonstrate that the expression of the SHMT2 gene responsible for serine metabolism and formation of folate one-carbon is controlled by ERN1 protein kinase and induced by hypoxia as well as glutamine and glucose deprivation conditions in glioblastoma cells and reflects the ERN1-mediated reprogramming of sensitivity this gene expression to nutrient deprivation.

Mapping myeloid cell function: Spatial diversity in tumor and neuronal microenvironment.

In this issue of Cancer Cell, Zhong et al. explore the dual role of TREM2 in glioblastoma-associated myeloid cells, demonstrating its function in promoting inflammation at the tumor-neural interface and suppression within the tumor core, influenced by the local microenvironment. These findings open up promising prospects for advancements in neuro-oncological immunotherapy.

Unveiling novel cell clusters and biomarkers in glioblastoma and its peritumoral microenvironment at the single-cell perspective.

BACKGROUND: Glioblastoma (GBM) is a highly heterogeneous, recurrent and aggressively invasive primary malignant brain tumor. The heterogeneity of GBM results in poor targeted therapy. Therefore, the aim of this study is to depict the cellular landscape of GBM and its peritumor from a single-cell perspective. Discovering new cell subtypes and biomarkers, and providing a theoretical basis for precision therapy. METHODS: We collected 8 tissue samples from 4 GBM patients to perform 10 × single-cell transcriptome sequencing. Quality control and filtering of data by Seurat package for clustering. Inferring copy number variations to identify malignant cells via the infercnv package. Functional enrichment analysis was performed by GSVA and clusterProfiler packages. STRING database and Cytoscape software were used to construct protein interaction networks. Inferring transcription factors by pySCENIC. Building cell differentiation trajectories via the monocle package. To infer intercellular communication networks by CellPhoneDB software. RESULTS: We observed that the tumor microenvironment (TME) varies among different locations and different GBM patients. We identified a proliferative cluster of oligodendrocytes with high expression of mitochondrial genes. We also identified two clusters of myeloid cells, one primarily located in the peritumor exhibiting an M1 phenotype with elevated TNFAIP8L3 expression, and another in the tumor and peritumor showing a proliferative tendency towards an M2 phenotype with increased DTL expression. We identified XIST, KCNH7, SYT1 and DIAPH3 as potential factors associated with the proliferation of malignant cells in GBM. CONCLUSIONS: These biomarkers and cell clusters we discovered may serve as targets for treatment. Targeted drugs developed against these biomarkers and cell clusters may enhance treatment efficacy, optimize immune therapy strategies, and improve the response rates of GBM patients to immunotherapy. Our findings provide a theoretical basis for the development of individualized treatment and precision medicine for GBM, which may be used to improve the survival of GBM patients.

Factors associated with changes in the quality of life and family functioning scores of primary caregivers of children and young people with primary brain tumors in Karachi, Pakistan: a prospective cohort study.

BACKGROUND: There are limited data available, particularly in low- and middle-income countries (LMICs), on the long-term quality of life (QoL) and family functioning of primary caregivers of children and young people (CYPs) affected by primary brain tumors (PBTs). This study aimed to assess the factors associated with the mean change in QoL and family functioning scores of primary caregivers of CYP patients with PBTs 12 months posttreatment. METHODS: This prospective cohort study enrolled CYPs aged 5-21 years with newly diagnosed PBTs and their primary caregivers. The study was carried out between November 2020 and July 2023. The primary caregivers of CYPs were recruited from two major tertiary care centers in Karachi, Pakistan. The primary caregivers QoL were assessed by the Pediatric Quality of Life Inventory (PedsQL) Family Impact Module. The assessment was undertaken by a psychologist at the time of diagnosis and 12 months posttreatment. The data were analyzed with STATA version 12. RESULTS: Forty-eight CYPs with newly diagnosed PBTs and their primary caregivers (46 mothers and 2 fathers) were enrolled. At 12 months posttreatment, 25 (52%) CYPs and their primary caregivers (mothers) were reassessed, and 23 (48%) were lost to follow-up. On multivariable analysis, a significant decrease in mothers' mean 12-month posttreatment QoL and family functioning scores was associated with CYP having posttreatment seizures (beta= -10.2; 95% CI: -18.4 to -2.0) and with the financial burden associated with the CYP's illness (beta= -0.3; 95% CI: -0.4 to -0.1). However, in those cases where CYP had higher posttreatment quality of life scores (beta = 0.4; 95% CI = 0.1, 0.6) and posttreatment higher verbal intelligence scores (beta = 0.1; 95% CI = 0.01, 0.3), the mothers' QoL and family functioning scores were significantly greater. CONCLUSION: We found a significant decrease in QoL of mothers who had a high financial burden and whose CYP had posttreatment seizures. However, those whose CYPs had higher posttreatment verbal intelligence scores and quality of life scores had significantly greater QoL scores. Identification of the factors that influence primary caregivers QoL has the potential to aid in the development of targeted strategies to alleviate stressors and improve the overall quality of life for primary caregivers and their children who are at high risk.

Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns.

PURPOSE: Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM). METHODS: Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method. RESULTS: Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days. CONCLUSION: We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.

Awake craniotomy with English and British sign language mapping in a patient with a left temporal glioblastoma reveals discordant speech-sign language maps.

The aim of this case study was to describe differences in English and British Sign Language (BSL) communication caused by a left temporal tumour resulting in discordant presentation of symptoms, intraoperative stimulation mapping during awake craniotomy and post-operative language abilities. We report the first case of a hearing child of deaf adults, who acquired BSL with English as a second language. The patient presented with English word finding difficulty, phonemic paraphasias, and reading and writing challenges, with BSL preserved. Intraoperatively, object naming and semantic fluency tasks were performed in English and BSL, revealing differential language maps for each modality. Post-operative assessment confirmed mild dysphasia for English with BSL preserved. These findings suggest that in hearing people who acquire a signed language as a first language, topographical organisation may differ to that of a second, spoken, language.

Breaking new ground: can artificial intelligence and machine learning transform papillary glioneuronal tumor diagnosis?

Papillary glioneuronal tumors (PGNTs), classified as Grade I by the WHO in 2016, present diagnostic challenges due to their rarity and potential for malignancy. Xiaodan Du et al.'s recent study of 36 confirmed PGNT cases provides critical insights into their imaging characteristics, revealing frequent presentation with headaches, seizures, and mass effect symptoms, predominantly located in the supratentorial region near the lateral ventricles. Lesions often appeared as mixed cystic and solid masses with septations or as cystic masses with mural nodules. Given these complexities, artificial intelligence (AI) and machine learning (ML) offer promising advancements for PGNT diagnosis. Previous studies have demonstrated AI's efficacy in diagnosing various brain tumors, utilizing deep learning and advanced imaging techniques for rapid and accurate identification. Implementing AI in PGNT diagnosis involves assembling comprehensive datasets, preprocessing data, extracting relevant features, and iteratively training models for optimal performance. Despite AI's potential, medical professionals must validate AI predictions, ensuring they complement rather than replace clinical expertise. This integration of AI and ML into PGNT diagnostics could significantly enhance preoperative accuracy, ultimately improving patient outcomes through more precise and timely interventions.

Host-defence caerin 1.1 and 1.9 peptides suppress glioblastoma U87 and U118 cell proliferation through the modulation of mitochondrial respiration and induce the downregulation of CHI3L1.

Glioblastoma, the most aggressive form of brain cancer, poses a significant global health challenge with a considerable mortality rate. With the predicted increase in glioblastoma incidence, there is an urgent need for more effective treatment strategies. In this study, we explore the potential of caerin 1.1 and 1.9, host defence peptides derived from an Australian tree frog, in inhibiting glioblastoma U87 and U118 cell growth. Our findings demonstrate the inhibitory impact of caerin 1.1 and 1.9 on cell growth through CCK8 assays. Additionally, these peptides effectively curtail the migration of glioblastoma cells in a cell scratch assay, exhibiting varying inhibitory effects among different cell lines. Notably, the peptides hinder the G0/S phase replication in both U87 and U118 cells, pointing to their impact on the cell cycle. Furthermore, caerin 1.1 and 1.9 show the ability to enter the cytoplasm of glioblastoma cells, influencing the morphology of mitochondria. Proteomics experiments reveal intriguing insights, with a decrease in CHI3L1 expression and an increase in PZP and JUNB expression after peptide treatment. These proteins play roles in cell energy metabolism and inflammatory response, suggesting a multifaceted impact on glioblastoma cells. In conclusion, our study underscores the substantial anticancer potential of caerin 1.1 and 1.9 against glioblastoma cells. These findings propose the peptides as promising candidates for further exploration in the realm of glioblastoma management, offering new avenues for developing effective treatment strategies.

Lomustine with or without reirradiation for first progression of glioblastoma, LEGATO, EORTC-2227-BTG: study protocol for a randomized phase III study.

BACKGROUND: Chemotherapy with lomustine is widely considered as standard treatment option for progressive glioblastoma. The value of adding radiotherapy to second-line chemotherapy is not known. METHODS: EORTC-2227-BTG (LEGATO, NCT05904119) is an investigator-initiated, pragmatic (PRECIS-2 score: 34 out of 45), randomized, multicenter phase III trial in patients with first progression of glioblastoma. A total of 411 patients will be randomized in a 1:1 ratio to lomustine (110 mg/m2 every 6 weeks) or lomustine (110 mg/m2 every 6weeks) plus radiotherapy (35 Gy in 10 fractions). Main eligibility criteria include histologic confirmation of glioblastoma, isocitrate dehydrogenase gene (IDH) wild-type per WHO 2021 classification, first progression at least 6 months after the end of prior radiotherapy, radiologically measurable disease according to RANO criteria with a maximum tumor diameter of 5 cm, and WHO performance status of 0-2. The primary efficacy endpoint is overall survival (OS) and secondary endpoints include progression-free survival, response rate, neurocognitive function, health-related quality of life, and health economic parameters. LEGATO is funded by the European Union's Horizon Europe Research program, was activated in March 2024 and will enroll patients in 43 sites in 11 countries across Europe with study completion projected in 2028. DISCUSSION: EORTC-2227-BTG (LEGATO) is a publicly funded pragmatic phase III trial designed to clarify the efficacy of adding reirradiation to chemotherapy with lomustine for the treatment of patients with first progression of glioblastoma. TRIAL REGISTRATION: ClinicalTrials.gov NCT05904119. Registered before start of inclusion, 23 May 2023.

Survival outcome and predictors of WHO grade 2 and 3 insular gliomas: A classification based on the tumor spread.

OBJECTIVE: The study aimed to identify if clinical features and survival outcomes of insular glioma patients are associated with our classification based on the tumor spread. METHODS: Our study included 283 consecutive patients diagnosed with histological grade 2 and 3 insular gliomas. A new classification was proposed, and tumors restricted to the paralimbic system were defined as type 1. When tumors invaded the limbic system (referred to as the hippocampus and its surrounding structures in this study) simultaneously, they were defined as type 2. Tumors with additional internal capsule involvement were defined as type 3. RESULTS: Tumors defined as type 3 had a higher age at diagnosis (p = 0.002) and a higher preoperative volume (p < 0.001). Furthermore, type 3 was more likely to be diagnosed as IDH wild type (p < 0.001), with a higher rate of Ki-67 index (p = 0.015) and a lower rate of gross total resection (p < 0.001). Type 1 had a slower tumor growth rate than type 2 (mean 3.3%/month vs. 19.8%/month; p < 0.001). Multivariate Cox regression analysis revealed the extent of resection (HR 0.259, p = 0.004), IDH status (HR 3.694, p = 0.012), and tumor spread type (HR = 1.874, p = 0.012) as independent predictors of overall survival (OS). Tumor grade (HR 2.609, p = 0.008), the extent of resection (HR 0.488, p = 0.038), IDH status (HR 2.225, p = 0.025), and tumor spread type (HR 1.531, p = 0.038) were significant in predicting progression-free survival (PFS). CONCLUSION: The current study proposes a classification of the insular glioma according to the tumor spread. It indicates that the tumors defined as type 1 have a relatively better nature and biological characteristics, and those defined as type 3 can be more aggressive and refractory. Besides its predictive value for prognosis, the classification has potential value in formulating surgical strategies for patients with insular gliomas.

[Long-term response to second-line treatment with sacituzumab govitecan in a patient affected by brain disease and early relpased TNBC.] / Risposta mantenuta al trattamento di seconda linea con sacituzumab govitecan in paziente con malattia encefalica e sistemica da TNBC.

The higher frequency of metastasization and poor prognosis of triple-negative breast cancer require suitable expertise in order to set up an appropriate and effective treatment plan for these patients. Our case describes the clinical history of a 63-year-old BRCA1/2 wild-type woman with excellent ECOG performance status and advanced PD-L1 negative breast cancer with brain, nodal and hepatic metastases. When occurred the brain progression within one year from neoadjuvant chemotherapy for a locally advanced tumor, the patient was treated with brain stereotaxis and a systemic platinum-based therapy that was not completed due to poor tolerance. Later instrumental examinations confirmed a new systemic and visceral progression, for which the patient underwent new therapy with sacituzumab govitecan (SG). During this treatment, we observed a reduction of the target liver and nodal lesions. The onset after several months of two very small cortico-subcortical metastases, on which stereotactic radiotherapy was performed, did not lead us to discontinuate the treatment, that was ongoing for another six months, with an excellent control both of brain and systemic disease without any symptoms, until a new disease progression at other sites requiring a therapeutic change. The use of antibody-drug conjugates allowed a significant prolongation of time to progression and overall survival in our clinical scenario characterized by poor prognosis due to early recurrence and brain involvement.