Neuro-oncology最新文献

Background: Nonfunctioning pituitary neuroendocrine tumors (NFPitNETs) account for ∼30-35% of PitNETs; ∼75% arise from the SF1 lineage. Recurrence remains common despite resection (∼30% in 10 years), and routine histopathology/IHC has limited value in predicting recurrence risk. This study evaluated whether DNA methylation profiling improves recurrence risk stratification.

Material and methods: Genome-wide tissue methylation (Illumina EPIC v1, 850K) was analyzed in 117 retrospective NFPitNETs with clinical and imaging follow-up. Unsupervised consensus clustering defined methylation-based subgroups, followed by supervised differential methylation analysis to identify cluster-specific differentially methylated probes (DMPs). A classifier was trained using these signatures, with predicted subgroup memberships correlated with regrowth and progression-free survival (PFS). To ensure reliable estimations, longitudinal mixed-effects models were restricted to the interval of model stability (∼9 years), reflecting cohort follow-up. External validation was performed in three independent cohorts.

Results: Five clusters (k1-k5) emerged: four SF1 positive-predominant (k1, k2, k3, k5) and one TPIT/PIT1-enriched NFPitNETs (k4). Among the 562 differentially methylated probes, many mapped to genes regulating cell-cycle and immune pathways. Compared with k1-k2, k3, k4, and k5 possessed significantly higher recurrence risk. Within SF1-lineage tumors, k3 exhibited postoperative tumor-volume expansion beginning at ∼6 years. The methylation-based classifier achieved ∼97% accuracy in assigning clusters and maintained prognostic separation across independent cohorts.

Conclusions: DNA methylation profiling identifies biologically and clinically distinct NFPitNET subgroups, particularly within the SF1 lineage, and may enhance prediction of recurrence risk. Prospective validation and demonstration of clinical utility are warranted to support integration into precision management workflows.

Background: This study aimed to identify prognostic factors in patients with newly diagnosed primary central nervous system lymphoma (PCNSL) treated with high-dose methotrexate-based therapy and to develop a novel risk-stratification model using easily measurable clinical and laboratory parameters.

Methods: A total of 451 patients with newly diagnosed PCNSL were identified from a prospective registry at Asan Medical Center, Seoul. Patients were randomly assigned to a training cohort (n = 280; October 2002-August 2019) and an independent validation cohort (n = 171; September 2019-December 2023).

Results: With a median follow-up of 106.0 months (95% CI, 101.0-120.0), the median overall survival (OS) in the training cohort was 46.1 months (95% CI, 34.9-57.6). Independent predictors of worse OS (p < 0.05) included age ≥65 years, high serum β2-microglobulin levels (≥1.8 mg/L), elevated serum lactate dehydrogenase, and ECOG performance status >1. These four factors were combined to form the ABLE score, which stratified patients into low- (0 risk factors), intermediate- (1 risk factor), and high-risk (≥2 risk factors) groups. In the training cohort, median OS was 109.0, 49.0, and 18.0 months, respectively (p < 0.001). Validation in the independent cohort confirmed significant prognostic discrimination, with median OS of not reached, 53.1, and 19.0 months for each risk group (p < 0.001). Comparative analyses demonstrated that the ABLE model showed improved discrimination compared with existing systems. Bootstrap validation (n = 451) yielded an optimism-corrected C-index of 0.656 (95% CI, 0.628-0.685).

Conclusions: The ABLE risk-stratification model can effectively differentiate prognostic subgroups in patients with PCNSL.

Background: Oncolytic virotherapy holds promise for glioblastoma, but the intratumoral replication kinetics of oncolytic viruses and resistance mechanisms of tumor cells remain poorly understood, limiting the development of precise combinssation strategies to improve durable efficacy.

Methods: Using the translational RESCUE framework that synchronizes clinical trials with patient-derived xenograft (PDX) models, we profiled the replication kinetics of the oncolytic adenovirus YSCH-01 and performed genome-wide CRISPR activation screening to identify key genes restricting sustained viral replication. Through spatial transcriptomics combined with histological analyses, we delineated the spatial determinants that limit viral dissemination following oncolytic virus administration.

Results: We identified BCL10 as a key suppressor of sustained viral replication. Viral infection activated the BCL10-NF-κB pathway, triggering paracrine secretion of interleukin-8 (IL-8) from infected tumor cells. IL-8 induced senescence and fibrotic remodeling in neighboring uninfected cells, forming a previously unrecognized Tumor Self-Rampart (TSR)-a concentric barrier of senescent and fibrotic tumor cells that spatially confines viral propagation. TSR was validated in both PDX and patient tumors. IL-8 blockade with Reparixin or peri-dosing glucocorticoids effectively disrupted TSR formation, prolonged viral persistence, and enhanced therapeutic efficacy.

Conclusion: Glioblastoma mounts a spatial self-protective defense through IL-8-driven TSR formation that restricts oncolytic virus spread. IL-8 functions as both a pharmacodynamic biomarker and a therapeutic target, and its inhibition provides a rational strategy to overcome resistance and optimize GBM virotherapy.

Background: Lung Cancer Leptomeningeal Metastasis (LC-LM) severely impacts patient survival and quality of life, yet current diagnostic methods lack sufficient sensitivity and specificity, particularly for early detection. Cerebrospinal fluid (CSF) metabolomics may reveal specific biomarkers reflecting brain metastasis.

Methods: We performed untargeted metabolomic profiling of CSF samples by high-resolution mass spectrometry (HRMS) in a cohort of 218 participants, including 99 samples from LC-LM (with cancer cells detected in the CSF), 12 samples from the lung cancer parenchymal brain metastases (with no cancer cells detected in the CSF), 27 samples from the control group, 21 samples from the breast cancer LM, 15 samples from patients with LM from other tumors such as melanoma and gastric cancer, and 36 samples from other diseases. Significant metabolites were identified and validated. Subsequently, targeted metabolomics was conducted on serum samples from an independent cohort (n = 233), including 50 LC-LM patients, 150 patients with primary lung cancer (stages I-III), and 33 benign pulmonary nodules.

Results: Untargeted CSF metabolomics revealed a distinct metabolic signature in LC-LM patients. Differential analysis identified metabolites significantly altered in LC-LM, notably elevated lactic acid, N1, N12-diacetylspermine, and altered amino acid metabolites (e.g., L-proline, L-glutamic acid), each demonstrating strong diagnostic accuracy individually, with area under the receiver operating characteristic (ROC) curve (AUC) > 0.90. Machine learning classification models based on CSF metabolite panels achieved perfect diagnostic performance (AUC = 1.00) in distinguishing LC-LM from controls and other groups. Targeted validation of five top metabolites in serum samples confirmed their diagnostic utility, with N1, N12-diacetylspermine achieving an AUC of 0.882, superior to traditional protein biomarkers.

Conclusion: CSF-based metabolomic profiling combined with machine learning offers a highly accurate and minimally invasive diagnostic tool for LC-LM. Serum validation further supports its translational potential, emphasizing its significance in clinical practice for improving early detection and potentially enhancing patient management and outcomes.