Neuro-oncology advances最新文献

Background: Cognitive impairment (CI) significantly impacts the quality of life of glioma patients. The main contributing risk factors include tumor characteristics, treatment-related factors, and their complex interplay. This review explores the role of advanced structural neuroimaging techniques in understanding CI in glioma patients.

Methods: A literature search was conducted in PubMed, PsycINFO, and ISI Web of Knowledge using specific keywords. We included studies with advanced magnetic resonance imaging techniques and objective neuropsychological exams.

Results: At diagnosis, during the pre-surgery phase, associations between glioma characteristics and cognitive outcomes have been described. Specifically, patients with isocitrate dehydrogenase (IDH)-wild-type gliomas exhibit more adverse cognitive outcomes, accompanied by disruptions in gray (GM) and white matter (WM) networks when compared to IDH-mutant. In addition, pre- and post-surgery imaging analyses highlight the importance of preserving specific WM tracts, such as the inferior longitudinal and arcuate fasciculus, in mitigating verbal memory and language processing decline. Furthermore, examining gliomas in perisylvian regions emphasizes deleterious effects on various cognitive domains. Additionally, it has been suggested that neuroplastic reorganization could serve as a compensatory mechanism against CI. Lastly, a limited number of studies suggest long-term CI linked to GM atrophy and leukoencephalopathy induced by radiotherapy ± chemotherapy in glioma survivors, highlighting the need for improving treatment approaches, particularly for patients with extended survival expectations.

Conclusion: This review underscores the need for nuanced understanding and an individual approach in the management of glioma patients. Neuroplastic insights offer clinicians valuable guidance in surgical decision-making and personalized therapeutic approaches thus improving patient outcomes in neuro-oncology.

Genome-wide DNA methylation signatures correlate with and distinguish central nervous system (CNS) tumor types. Since the publication of the initial CNS tumor DNA methylation classifier in 2018, this platform has been increasingly used as a diagnostic tool for CNS tumors, with multiple studies showing the value and utility of DNA methylation-based classification of CNS tumors. A Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) Working Group was therefore convened to describe the current state of the field and to provide advice based on lessons learned to date. Here, we provide recommendations for the use of DNA methylation-based classification in CNS tumor diagnostics, emphasizing the attributes and limitations of the modality. We emphasize that the methylation classifier is one diagnostic tool to be used alongside previously established diagnostic tools in a fully integrated fashion. In addition, we provide examples of the inclusion of DNA methylation data within the layered diagnostic reporting format endorsed by the World Health Organization (WHO) and the International Collaboration on Cancer Reporting. We emphasize the need for backward compatibility of future platforms to enable accumulated data to be compatible with new versions of the array. Finally, we outline the specific connections between methylation classes and CNS WHO tumor types to aid in the interpretation of classifier results. It is hoped that this update will assist the neuro-oncology community in the interpretation of DNA methylation classifier results to facilitate the accurate diagnosis of CNS tumors and thereby help guide patient management.

Background: Despite improvements in our understanding of glioblastoma pathophysiology, there have been no major improvements in treatment in recent years. Animal models are a vital tool for investigating cancer biology and its treatment, but have known limitations. There have been advances in glioblastoma modeling techniques in this century although it is unclear to what extent they have been adopted.

Methods: We searched Pubmed and EMBASE using terms designed to identify all publications reporting an animal glioma experiment, using a machine learning algorithm to assist with screening. We reviewed the full text of a sample of 1000 articles and then used the findings to inform a screen of all included abstracts to appraise the modeling applications across the entire dataset.

Results: The search identified 26 201 publications of which 13 783 were included at screening. The automated screening had high sensitivity but limited specificity. We observed a dominance of traditional cell line paradigms and the emergence of advanced tumor model systems eclipsed by a large increase in the volume of cell line experiments. Few studies used more than 1 model in vivo and most publications did not verify critical genetic features.

Conclusions: Advanced models have clear advantages in terms of tumor and disease recapitulation and have largely not replaced traditional cell lines which have a number of critical deficiencies that limit their viability in modern animal research. The judicious use of advanced models or more relevant cell lines might improve the translational relevance of future animal glioblastoma experimentation.

Background: Approximately half of the isocitrate dehydrogenase (IDH)-wildtype glioblastomas (GBMs) exhibit EGFR amplification. Additionally, genomic changes that occur in the extracellular domain of EGFR can lead to ligand-hypersensitivity (R108K/A289V/G598V) or ligand-independence (EGFRvIII). Unlike in lung adenocarcinoma (LUAD), clinical trials with epidermal growth factor receptor (EGFR) inhibitors showed no survival benefit for GBM and it remains unclear why. We aimed to elucidate differences in molecular mechanisms of EGFR activation and regulation between GBM and LUAD.

Methods: We used RNA-sequencing (RNA-seq) data to find EGFR co-regulated genes and pathways in GBM and compare EGFR signaling patterns between GBM and LUAD. Cellular origins of expression signals were determined by analyzing single-cell RNA-seq data.

Results: We identified 2 ligands (BTC/EREG) among the significant EGFR predictor genes (TCGA-GBM: n = 169, Intellance-2: n = 166). Their expression was inversely correlated with EGFR amplification and incidence of ligand-sensitive mutations. Ligands were expressed by nonmalignant cells and differed in their primary source of expression (BTC: neurons, EREG: myeloid). High expression of MDM2 and CDK4 was less common in EGFR-amplified GBMs with ligand-sensitive mutations compared with those without these mutations. Our analyses revealed distinct transcriptional profiles between GBM and LUAD when comparing tumors carrying activating mutations.

Conclusions: BTC and EREG are negatively associated with EGFR expression in GBM. These findings emphasize the role of ligands in regulating EGFR, where EGFR activation seems to be modulated by the highly varying levels of EGFR amplification, the sensitivity of the receptor toward ligands, and ligand expression levels. Ligand expression levels and EGFR mutations could refine patient stratification for EGFR-targeted therapies in GBM.

Background: Adult-type diffuse gliomas encompass nearly a quarter of all primary tumors found in the CNS, including astrocytoma, oligodendroglioma, and glioblastoma. Histopathological tumor grade and molecular profile distinctly impact patient survival. Despite treatment advancements, patients with recurrent glioma have a very poor clinical outcome, warranting improved risk stratification to determine therapeutic interventions. Various studies have shown that copper is a notable trace element that is crucial for biological processes and has been shown to display pro-tumorigenic functions in cancer, particularly gliomas.

Methods: Differential gene expression, Cox regression, and least absolute shrinkage and selection operator regression were used to identify 19 copper-homeostasis-related gene signatures using TCGA lower-grade glioma and glioblastoma (GBM) cohorts. The GLASS Consortium dataset was used as an independent validation cohort. Enrichment analysis revealed the involvement of the signature in various cancer-related pathways and biological processes. Using this CHRG signature, a risk score model and a nomogram were developed to predict survival in glioma patients.

Results: Our prognostic CHRG signature stratified patients into high- and low-risk groups, demonstrating robust predictive performance. High-risk groups showed poorer survival outcomes. The nomogram model integrating CHRG signature and clinical features accurately predicted 1-, 3-, and 5-year survival rates in both training and test sets.

Conclusions: The identified 19-gene CHRG signature holds promise as a prognostic tool, enabling accurate risk stratification and survival prediction in glioma patients. Integrating this signature with clinical characteristics enhances prognostic accuracy, underscoring its potential clinical utility in optimizing therapeutic strategies and patient care in glioma management.

Background: This study aimed to explore the potential of the Advanced Data Analytics (ADA) package of GPT-4 to autonomously develop machine learning models (MLMs) for predicting glioma molecular types using radiomics from MRI.

Methods: Radiomic features were extracted from preoperative MRI of n = 615 newly diagnosed glioma patients to predict glioma molecular types (IDH-wildtype vs IDH-mutant 1p19q-codeleted vs IDH-mutant 1p19q-non-codeleted) with a multiclass ML approach. Specifically, ADA was used to autonomously develop an ML pipeline and benchmark performance against an established handcrafted model using various MRI normalization methods (N4, Zscore, and WhiteStripe). External validation was performed on 2 public glioma datasets D2 (n = 160) and D3 (n = 410).

Results: GPT-4 achieved the highest accuracy of 0.820 (95% CI = 0.819-0.821) on the D3 dataset with N4/WS normalization, significantly outperforming the benchmark model's accuracy of 0.678 (95% CI = 0.677-0.680) (P < .001). Class-wise analysis showed performance variations across different glioma types. In the IDH-wildtype group, GPT-4 had a recall of 0.997 (95% CI = 0.997-0.997), surpassing the benchmark's 0.742 (95% CI = 0.740-0.743). For the IDH-mut 1p/19q-non-codel group, GPT-4's recall was 0.275 (95% CI = 0.272-0.279), lower than the benchmark's 0.426 (95% CI = 0.423-0.430). In the IDH-mut 1p/19q-codel group, GPT-4's recall was 0.199 (95% CI = 0.191-0.206), below the benchmark's 0.730 (95% CI = 0.721-0.738). On the D2 dataset, GPT-4's accuracy was significantly lower (P < .001) than the benchmark's, with N4/WS achieving 0.668 (95% CI = 0.666-0.671) compared with 0.719 (95% CI = 0.717-0.722) (P < .001). Class-wise analysis revealed the same pattern as observed in D3.

Conclusions: GPT-4 can autonomously develop radiomics-based MLMs, achieving performance comparable to handcrafted MLMs. However, its poorer class-wise performance due to unbalanced datasets shows limitations in handling complete end-to-end ML pipelines.

Background: Intrathecal (IT) chemotherapy is essential in treating hematological malignancies, but it can lead to ascending paraplegia, a condition that currently lacks clear management guidelines.

Methods: We conducted a systematic review, analyzing 1219 studies and 116 patients, adhering to PRISMA guidelines for individual patient data. The study, registered under PROSPERO (CRD42022362121), focused on the onset, diagnostic approaches, and therapeutic interventions associated with this complication, and management strategies to tackle the ascending paraplegia.

Results: Paraplegia typically manifests approximately 10 days after chemotherapy, irrespective of injection frequency. In 95% of cases, paralysis stabilizes around the umbilical region, although some patients progress to upper limb involvement and respiratory compromise. Despite various diagnostic methods, consistent inflammatory markers in blood or cerebrospinal fluid are lacking, with approximately 60% of patients showing normal magnetic resonance imaging results at presentation. Misdiagnoses often include transverse myelitis, Guillain-Barré syndrome, and autoimmune radiculitis. Common treatments such as corticosteroids and intravenous immunoglobulins show limited effectiveness.

Conclusion: Our review delineates the clinical entity of ascending paraplegia following IT chemotherapy, aiming to increase clinician awareness and provide prognostic insight. We introduce the term post-IT paraplegia syndrome to facilitate accurate diagnosis and optimize treatment strategies for affected patients.

Background: Diffuse IDH mutant low-grade gliomas (IDHmt LGG) (World Health Organization grade 2) typically affect young adults. The outcome is variable, with survival ranging from 5 to over 20 years. The timing and choice of initial treatments after surgery remain controversial. In particular, radiotherapy is associated with early and late cognitive toxicity. Over 90% of IDHmt LGG exhibit some degree of promoter methylation of the repair gene O(6)-methylguanine-DNA methytransferase (MGMTp) that when expressed blunts the effect of alkylating agent chemotherapy, for example, temozolomide (TMZ). However, the clinical value of MGMTp methylation predicting benefit from TMZ in IDHmt LGG is unclear.

Methods: Patients treated in the EORTC-22033 phase III trial comparing TMZ versus radiotherapy served as training set to establish a cutoff based on the MGMT-STP27 methylation score. A validation cohort was established with patients treated in a single-center first-line with TMZ after surgery/surgeries.

Results: The MGMT-STP27 methylation score was associated with better progression-free survival (PFS) in the training cohort treated with TMZ, but not radiotherapy. In the validation cohort, an association with next treatment-free survival (P = .045) after TMZ was observed, and a trend using RANO criteria (P = .07). A cutoff value set above the 95% confidence interval of being methylated was significantly associated with PFS in the TMZ-treated training cohort, but not in the radiotherapy arm. However, this cutoff could not be confirmed in the test cohort.

Conclusions: While the MGMTp methylation score was associated with better outcomes in TMZ-treated IDHmt LGG, a cutoff could not be established to guide treatment decisions.

Background: Mutant isocitrate dehydrogenase (IDHm) inhibitors represent a novel targeted approach for treating IDHm glioma patients, yet their optimal use in clinical practice outside of clinical trials remains undefined. This study describes the real-world utilization of the mutant IDH1 inhibitor (IDHi), ivosidenib, in patients with IDHm glioma.

Methods: We retrospectively reviewed clinical and radiographic data from patients with IDHm glioma treated with ivosidenib monotherapy from 2020 to 2024 at the Dana-Farber Cancer Institute and Massachusetts General Hospital.

Results: This cohort included 74 patients with a median age of 39. There were 35 astrocytomas and 39 oligodendrogliomas, with 49, 23, and 2, grade 2, 3, and 4 tumors, respectively. Nineteen patients (26%) experienced an adverse event, although only 1 patient discontinued ivosidenib for adverse events. Median progression-free survival was 31 months and median overall survival was not reached. Seven patients (9%) had partial response, 3 (4%) had minor response, 47 (64%) had stable disease, and 17 (23%) had progressive disease. The presence of enhancing disease at ivosidenib initiation was associated with lower disease control rates (DCR) whereas DCR differences were not detected based on grade (grade 2 vs. 3), tumor histology, or age. Subsequent-line ivosidenib use had lower DCR although this may have been explained by enrichment of patients with enhancing disease.

Conclusions: In this large cohort of IDHm glioma patients, ivosidenib was well tolerated. Our results support the use of IDHi therapy in patients with grade 2 or 3 astrocytoma or oligodendroglioma and highlight limited effectiveness in patients with enhancing disease.

Background: Laser interstitial thermal therapy (LITT) is a minimally invasive surgical treatment being employed frequently for radiographically progressive brain metastases. Considerable interest exists in combining LITT-mediated in situ vaccination to license immune checkpoint blockade (ICB). No studies have examined the clinical feasibility of this combination in brain metastases.

Methods: All patients receiving LITT for radiographically progressive non-small cell lung carcinoma (NSCLC) brain metastases at a single center from 2015 to 2023 were retrospectively reviewed. Combination therapy was defined as ICB within 6 weeks of LITT. Clinical data, post-LITT freedom from local progression, and overall survival (OS) were collected. Adverse events (AEs) were evaluated according to Common Terminology Criteria.

Results: Eighteen patients received LITT + ICB for a total of 19 lesions. The median time between therapies was 2.29 weeks (range 0.85-5.98). In comparison to NSCLC patients receiving LITT alone or with targeted therapy (LITT only) (n = 25), there was no decrement in procedural outcomes. Patients receiving LITT + ICB discontinued steroids at a median of 11 (4-147) days post-LITT vs. 24 (3-242) days for patients receiving LITT only (P = .62). At study cutoff, the local control rate was 18/19 (94.7%) lesions in the LITT + ICB group and 22/25 (88.0%) in the LITT only group. There were 3 and 5 AEs ≥Grade 3 in the LITT + ICB and LITT-only groups, respectively.

Conclusions: Combination of LITT and ICB does not compromise procedural outcomes or time to steroid discontinuation in NSCLC. Prospective studies are needed to assess biomarkers of immune response.