The <scp>WHO</scp> classification of tumors of the central nervous system‐finally here, and welcome!

Rodríguez, Fausto J.

doi:10.1111/bpa.13077

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…The accurate diagnosis and classification of gliomas are essential for improving patient clinical outcomes [ 2 , 3 ]. The WHO 2021 Classification of CNS tumors has integrated molecular data into the typing, subtyping, and grading of gliomas, thus improving the ability of clinicians to select the most appropriate therapies and design clinical trials [ 1 , 4 , 5 ]. However, these advances are mainly obtained from studies of brain gliomas [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Genomic profiling and prognostic factors of H3 K27M‐mutant spinal cord diffuse glioma

Chai

Yan

et al. 2023

Brain Pathology

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H3 K27-altered diffuse midline glioma is a highly lethal pediatric-type tumor without efficacious treatments. Recent findings have highlighted the heterogeneity among diffuse midline gliomas with different locations and ages. Compared to tumors located in the brain stem and thalamus, the molecular and clinicopathological features of H3 K27-altered spinal cord glioma are still largely elusive, thus hindering the accurate management of patients. Here we aimed to characterize the clinicopathological and molecular features of H3 K27M-mutant spinal cord glioma in 77 consecutive cases. We found that the H3 K27M-mutant spinal cord glioma, with a median age of 35 years old, had a significantly better prognosis than H3 K27M-mutant brain tumors. We noticed a molecular heterogeneity of H3 K27M-mutant spinal cord astrocytoma via targeted sequencing with 34 cases. TP53 mutation which occurred in 58.8% of cases is mutually exclusive with PPM1D (26%) and NF1 (44%) mutations. The TP53-mutant cases had a significantly higher number of copy number variants (CNV) and a marginally higher proportion of pediatric patients (age at diagnosis <18 years old, p = 0.056). Cox regression and Kaplan-Meier curve analysis showed that the higher number of CNV events (≥3), chromosome (Chr) 9p deletion, Chr 10p deletion, ATRX mutation, CDK6 amplification, and retinoblastoma protein (RB) pathway alteration are associated with worse survival. Cox regression analysis with clinicopathological features showed that glioblastoma histological type and a high Ki-67 index (>10%) are associated with a worse prognosis. Interestingly, the histological type, an independent prognostic factor in multivariate Cox regression, can also stratify molecular features of H3 K27M-mutant spinal cord glioma, including the RB pathway, KRAS/PI3K pathway, and chromosome arms CNV. In conclusion, although all H3 K27M-mutant spinal cord diffuse glioma were diagnosed as WHO Grade 4, the histological type, molecular features representing chromatin instability, and molecular alterations associated with accelerated cell proliferative activity should not be ignored in clinical management.Rui-Chao Chai and Hao Yan contributed equally to this study.

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Section: Introductionmentioning

confidence: 99%

Genomic profiling and prognostic factors of H3 K27M‐mutant spinal cord diffuse glioma

Chai

Yan

et al. 2023

Brain Pathology

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Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression

Afify,

Mohammed,

Hassanien

2024

Complex Intell. Syst.

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Leveraging deep learning (DL) approaches in genomics data has led to significant advances in cancer prediction. The continuous availability of gene expression datasets over the preceding years has made them one of the most accessible sources of genome-wide data, advancing cancer bioinformatics research and advanced prediction of cancer genomic data. To contribute to this topic, the proposed work is based on DL prediction in both convolutional neural network (CNN) and recurrent neural network (RNN) for five classes in brain cancer using gene expression data obtained from Curated Microarray Database (CuMiDa). This database is used for cancer classification and is publicly accessible on the official CuMiDa website. This paper implemented DL approaches using a One Dimensional-Convolutional Neural Network (1D-CNN) followed by an RNN classifier with and without Bayesian hyperparameter optimization (BO). The accuracy of this hybrid model combination of (BO + 1D-CNN + RNN) produced the highest classification accuracy of 100% instead of the 95% for the ML model in prior work and 90% for the (1D-CNN + RNN) algorithm considered in the paper. Therefore, the classification of brain cancer gene expression according to the hybrid model (BO + 1D-CNN + RNN) provides more accurate and useful assessments for patients with different types of brain cancers. Thus, gene expression data are used to create a DL classification-based- hybrid model that will hold senior promise in the treatment of brain cancer.

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Navigating Illumina DNA methylation data: biology versus technical artefacts

Glaser,

Kretzmer,

Kolassa

et al. 2024

NAR Genomics and Bioinformatics

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Illumina-based BeadChip arrays have revolutionized genome-wide DNA methylation profiling, pushing it into diagnostics. However, comprehensive quality assessment remains challenging within a wide range of available tissue materials and sample preparation methods. This study tackles two critical issues: differentiating between biological effects and technical artefacts in suboptimal quality samples and the impact of the first sample on the Illumina-like normalization algorithm. We introduce three quality control scores based on global DNA methylation distribution (DB-Score), bin distance from copy number variation analysis (BIN-Score) and consistently methylated CpGs (CM-Score) that rely on biological features rather than internal array controls. These scores, designed to be adjustable for different analysis tools and sample cohort characteristics, were explored and benchmarked across independent cohorts. Additionally, we reveal deviations in beta values caused by different sample rankings with the Illumina-like normalization algorithm, verified these with whole-genome methylation sequencing data and showed effects on differential DNA methylation analysis. Our findings underscore the necessity of consistently utilizing a pre-defined normalization sample within the ranking process to boost reproducibility of the Illumina-like normalization algorithm. Overall, our study delivers valuable insights, practical recommendations and R functions designed to enhance reproducibility and quality assurance of DNA methylation analysis, particularly for challenging sample types.

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The WHO classification of tumors of the central nervous system‐finally here, and welcome!

Cited by 4 publications

References 8 publications

Genomic profiling and prognostic factors of H3 K27M‐mutant spinal cord diffuse glioma

Genomic profiling and prognostic factors of H3 K27M‐mutant spinal cord diffuse glioma

Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression

Navigating Illumina DNA methylation data: biology versus technical artefacts

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