Search

Aims: The methylation status of the O6‐methylguanine‐DNA methyltransferase (MGMT) promoter region is essential in evaluating the prognosis and predicting the drug response in patients with glioblastoma. In this study, we evaluated the utility of using nanopore long‐read sequencing as a method for assessing methylation levels throughout the MGMT CpG‐island, compared its performance to established techniques and demonstrated its clinical applicability. Methods: We analysed 165 samples from CNS tumours, focusing on the MGMT CpG‐island using nanopore sequencing. Oxford Nanopore Technologies (ONT) MinION and PromethION flow cells were employed for single sample or barcoded assays, guided by a CRISPR/Cas9 protocol, adaptive sampling or as part of a whole genome sequencing assay. Methylation data obtained through nanopore sequencing were compared to results obtained via pyrosequencing and methylation bead arrays. Hierarchical clustering was applied to nanopore sequencing data for patient stratification. Results: Nanopore sequencing displayed a strong correlation (R2 = 0.91) with pyrosequencing results for the four CpGs of MGMT analysed by both methods. The MGMT‐STP27 algorithm's classification was effectively reproduced using nanopore data. Unsupervised hierarchical clustering revealed distinct patterns in methylated and unmethylated samples, providing comparable survival prediction capabilities. Nanopore sequencing yielded high‐confidence results in a rapid timeframe, typically within hours of sequencing, and extended the analysis to all 98 CpGs of the MGMT CpG‐island. Conclusions: This study presents nanopore sequencing as a valid and efficient method for determining MGMT promotor methylation status. It offers a comprehensive view of the MGMT promoter methylation landscape, which enables the identification of potentially clinically relevant subgroups of patients. Further exploration of the clinical implications of patient stratification using nanopore sequencing of MGMT is warranted. [ABSTRACT FROM AUTHOR]

An obstacle to effective uniform treatment of glioblastoma, especially at recurrence, is genetic and cellular intertumoral heterogeneity. Hence, personalized strategies are necessary, as are means to stratify potential targeted therapies in a clinically relevant timeframe. Functional profiling of drug candidates against patient-derived glioblastoma organoids (PD-GBO) holds promise as an empirical method to preclinically discover potentially effective treatments of individual tumors. Here, we describe our establishment of a PD-GBO-based functional profiling platform and the results of its application to four patient tumors. We show that our PD-GBO model system preserves key features of individual patient glioblastomas in vivo. As proof of concept, we tested a panel of 41 FDA-approved drugs and were able to identify potential treatment options for three out of four patients; the turnaround from tumor resection to discovery of treatment option was 13, 14, and 15 days, respectively. These results demonstrate that this approach is a complement and, potentially, an alternative to current molecular profiling efforts in the pursuit of effective personalized treatment discovery in a clinically relevant time period. Furthermore, these results warrant the use of PD-GBO platforms for preclinical identification of new drugs against defined morphological glioblastoma features. [ABSTRACT FROM AUTHOR]

The glioma samples run with the Rapid CNS A panel as target region were each loaded onto FLO-MIN106 R9.4.1 flow cells and sequencing was controlled in real-time by ReadFish (using a GPU powered consumer notebook). "PANEL A" indicates samples sequenced using Rapid CNS A, and "PANEL B" indicates samples sequenced using Rapid CNS B (sample by Rapid-CNS2 and 850 k identified as AT/RT excluded, depicted in Supplementary Fig. One sample identified as a glioma by histology was correctly classified as an atypical teratoid/rhabdoid tumor by Rapid-CNS SP 2 sp (confirmed by EPIC array analysis, Supplementary Fig. [Extracted from the article]

Phytomedicine has often been used as "alternative therapy," which in our opinion is unfortunate as it prevents its main actions being systematically studied, side effects explored, and toxicity tested, like all single-compound-based medicine. Our group is interested in finding which traditional or modern phytomedicines actually work and which are simply "working" through placebo, standardizing phytomedicine preparations, studying their toxicity, and finding active molecules in plants for modification and chemical synthesis as single compounds. Although fluctuation in efficacy due to seasonal and geographical variations in phytomedicine remains a concern, if well regulated, even plant extracts without isolated compounds can serve medicinal needs where single-compound options are currently not great. A potential concern with such phytomedicine is frequent mixing of ingredients in commercial formulations without test of synergism. Our study on the use of 2 traditional plants for Parkinson disease shows a clear lack of synergism, and to study nonsynergism better, we developed a new visualization approach. In this commentary, using our study on Parkinson disease as an example, we make a case for better evaluation of phytomedicines, especially testing for synergistic interactions. We also critique our own exploration of oxidative stress and few behavioral parameters alone to lay grounds for what we and hopefully others can do in future to extract more information from their phytomedicine studies. We hope this commentary acts as a good warning for anyone mixing 2 phytomedicines without testing. [ABSTRACT FROM AUTHOR]

Purpose: Primary central nervous system (CNS) gliomas can be classified by characteristic genetic alterations. In addition to solid tissue obtained via surgery or biopsy, cell-free DNA (cfDNA) from cerebrospinal fluid (CSF) is an alternative source of material for genomic analyses., Experimental Design: We performed targeted next-generation sequencing of CSF cfDNA in a representative cohort of 85 patients presenting at two neurooncological centers with suspicion of primary or recurrent glioma. Copy-number variation (CNV) profiles, single-nucleotide variants (SNV), and small insertions/deletions (indel) were combined into a molecular-guided tumor classification. Comparison with the solid tumor was performed for 38 cases with matching solid tissue available., Results: Cases were stratified into four groups: glioblastoma (n = 32), other glioma (n = 19), nonmalignant (n = 17), and nondiagnostic (n = 17). We introduced a molecular-guided tumor classification, which enabled identification of tumor entities and/or cancer-specific alterations in 75.0% (n = 24) of glioblastoma and 52.6% (n = 10) of other glioma cases. The overlap between CSF and matching solid tissue was highest for CNVs (26%-48%) and SNVs at predefined gene loci (44%), followed by SNVs/indels identified via uninformed variant calling (8%-14%). A molecular-guided tumor classification was possible for 23.5% (n = 4) of nondiagnostic cases., Conclusions: We developed a targeted sequencing workflow for CSF cfDNA as well as a strategy for interpretation and reporting of sequencing results based on a molecular-guided tumor classification in glioma. See related commentary by Abdullah, p. 2860., (©2024 The Authors; Published by the American Association for Cancer Research.)