Explore the vast range of titles available.

Background The 2021 WHO Classification of Central Nervous System Tumors introduces more molecular markers for glioma reclassification, including TERT promoter (TERTp) mutation as a key feature in glioblastoma diagnosis. Aims Given the changes in the entities included in each subtype under the new classification, this research investigated the distribution, prognostic value, and correlations with other molecular alterations of TERTp mutation in different subgroups under this latest classification. Methods All glioma patients admitted to Peking Union Medical College Hospital for surgical resection or biopsy from 2011 to 2022 were included. Samples were analyzed for TERTp mutation and 59 other gene alterations and chromosome copy number variations. Results A total of 207 patients were included. The occurrence of TERTp mutations varied with percentages of 4.55%, 100%, and 77.92% in astrocytoma, oligodendroglioma, and glioblastoma, respectively. 65% of all adult‐type glioma patients and 42.6% of IDH‐wildtype histology grade 2 or 3 patients were TERTp‐mutant. Survival analysis showed that TERTp mutation was a predictor of better prognosis in IDH‐mutant grade 2 gliomas (median OS (mOS): not reached (NA) (95% CI: NA–NA) vs. 75.9 (95% CI: 55.4–NA) months, HR = 0.077 (95% CI: 0.01–0.64), p = 0.003), while poor OS was associated with all Grade 4 gliomas (mOS: 17.5 (95% CI: 12.6–24.2) vs. 40.5 (95% CI: 24.4–83.8) months, HR = 2.014 (95% CI: 1.17–3.47), p = 0.01) and all IDH‐wildtype histology grade 2 or 3 gliomas (median OS: 12.6 (95% CI: 11–24.2) vs. 83.8 (95% CI: 35.2–NA) months, HR = 3.768 (95% CI: 1.83–7.78), p < 0.001). Moreover, TERTp mutation tended to co‐occur with EGFR, KRAS, and MET in glioblastoma. In the IDH‐mutant subgroup, it tended to co‐occur with CIC and FUBP1 alterations, while being mutually exclusive with ATRX and TP53 alterations. These correlations may further refine prognostic predictions. Given the big changes in the entities included in each subtype under the 2021 CNS tumor classification, the effect of TERTp mutation on prognosis and the relationship between TERTp mutation and other molecular alterations in different subgroups should be reevaluated. In this research, we investigated the distribution, prognostic value, and molecular correlation of TERTp mutation under this latest classification. Survival analysis showed that TERTp mutation was a predictor of better prognosis in IDH‐mutant grade 2 gliomas, while poor OS was associated with all Grade 4 gliomas and all IDH‐wildtype histology grade 2 or 3 gliomas. The correlation between TERTp mutation and other molecular alterations was multiform in different subgroups, which needs to be investigated further.

Low-grade gliomas (GII) inescapably progress to high-grade gliomas (GIII). The duration of this transition is highly variable between patients and reliable predictive markers do not exist. We noticed in a subset of cases of GII, obtained by awake neurosurgery, the presence of microfoci with high cellular density, high vascular density, or minimal endothelial proliferation, which we called GII+. Our aim was to investigate whether these foci display immunohistochemical and molecular characteristics similar to GIII and whether their presence is correlated to poor prognosis. We analyzed cell proliferation, hypoxia, vascularization, and alterations of tumorigenic pathways by immunohistochemistry (Ki-67, CD31, HIF-1-alpha, EGFR, P-AKT, P53, MDM2) and fluorescence in situ hybridization (EGFR, MDM2, PDGFRA) in the hypercellular foci of 16 GII+ cases. We compared overall survival between GII, GII+, and GIII. Ki-67, and CD31 expression was higher in the foci than in the tumor background in all cases. Aberrant expression of protein markers and genomic aberrations were also observed in some foci, distinct from the tumor background. Survival was shorter in GII+ than in GII cases. Our results suggest that these foci are the early histological hallmark of anaplastic transformation, which is supported by molecular aberrations. Our study is the first to demonstrate intratumoral morphological, immunohistochemical, and molecular heterogeneity in resection specimens of GII, the presence of which is correlated to shorter survival. Our findings question the discriminative capacity of the current glioma classification and provide arguments in favor of the creation of a grade intermediate between GII and GIII, to optimize the treatment strategy of GII.

The WHO 2007 glioma classification system (based primarily on tumour histology) resulted in considerable interobserver variability and substantial variation in patient survival within grades. Furthermore, few risk factors for glioma were known. Discoveries over the past decade have deepened our understanding of the molecular alterations underlying glioma and have led to the identification of numerous genetic risk factors. The advances in molecular characterization of glioma have reframed our understanding of its biology and led to the development of a new classification system for glioma. The WHO 2016 classification system comprises five glioma subtypes, categorized by both tumour morphology and molecular genetic information, which led to reduced misclassification and improved consistency of outcomes within glioma subtypes. To date, 25 risk loci for glioma have been identified and several rare inherited mutations that might cause glioma in some families have been discovered. This Review focuses on the two dominant trends in glioma science: the characterization of diagnostic and prognostic tumour markers and the identification of genetic and other risk factors. An overview of the many challenges still facing glioma researchers is also included.

Key Points The 2016 WHO Classification of Tumours of the Central Nervous System reflects a paradigm shift, replacing traditional histology-based glioma diagnostics with an integrated histological and molecular classification system that enables more-precise tumour categorization The requisite diagnostic biomarkers in the 2016 WHO classification of gliomas are IDH1/2 (IDH) mutations, 1p/19q codeletion, H3F3A or HIST1H3B/C K27M (H3-K27M) mutations and C11orf95–RELA fusions Additional diagnostically relevant biomarkers include loss of nuclear ATRX expression, TERT -promoter mutations, KIAA1549–BRAF fusions, BRAF -V600E mutation, H3F3A -G34 mutation, and several other alterations associated with rare glioma entities MGMT -promoter methylation is predictive of benefit from alkylating chemotherapy in patients with IDH-wild-type glioblastoma; predictive biomarkers for targeted therapies, such as IDH1 and BRAF mutations, are also emerging Novel methods for large-scale DNA-methylation, copy-number and mutational profiling will further advance the assessment of glioma-associated molecular biomarkers Clinical trials require assessment of molecular biomarkers as criteria for study entry and/or patient stratification; predictive DNA sequencing followed by targeted therapy will support the implementation of precision medicine in neuro-oncology In 2016, a revised WHO classification of glioma was published, in which molecular data and traditional histological information are incorporated into integrated diagnoses. Herein, the authors highlight the developments in our understanding of the molecular genetics of gliomas that underlie this classification, and review the current landscape of molecular biomarkers used in the classification of disease subtypes. In addition, they discuss how these advances can promote the development of novel pathogenesis-based therapeutic approaches, paving the way to precision medicine. Genome-wide molecular-profiling studies have revealed the characteristic genetic alterations and epigenetic profiles associated with different types of gliomas. These molecular characteristics can be used to refine glioma classification, to improve prediction of patient outcomes, and to guide individualized treatment. Thus, the WHO Classification of Tumours of the Central Nervous System was revised in 2016 to incorporate molecular biomarkers — together with classic histological features — in an integrated diagnosis, in order to define distinct glioma entities as precisely as possible. This paradigm shift is markedly changing how glioma is diagnosed, and has important implications for future clinical trials and patient management in daily practice. Herein, we highlight the developments in our understanding of the molecular genetics of gliomas, and review the current landscape of clinically relevant molecular biomarkers for use in classification of the disease subtypes. Novel approaches to the genetic characterization of gliomas based on large-scale DNA-methylation profiling and next-generation sequencing are also discussed. In addition, we illustrate how advances in the molecular genetics of gliomas can promote the development and clinical translation of novel pathogenesis-based therapeutic approaches, thereby paving the way towards precision medicine in neuro-oncology.

Glioma resection is associated with prolonged survival, but neuro-oncological trials have frequently refrained from quantifying the extent of resection. The Response Assessment in Neuro-Oncology (RANO) resect group is an international, multidisciplinary group that aims to standardise research practice by delineating the oncological role of surgery in diffuse adult-type gliomas as defined per WHO 2021 classification. Favourable survival effects of more extensive resection unfold over months to decades depending on the molecular tumour profile. In tumours with a more aggressive natural history, supramaximal resection might correlate with additional survival benefit. Weighing the expected survival benefits of resection as dictated by molecular tumour profiles against clinical factors, including the introduction of neurological deficits, we propose an algorithm to estimate the oncological effects of surgery for newly diagnosed gliomas. The algorithm serves to select patients who might benefit most from extensive resection and to emphasise the relevance of quantifying the extent of resection in clinical trials.

Single-cell RNA sequencing identifies a common origin for specific types of human glioma brain tumors. Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science , this issue p. eaai8478 Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)–mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses.

Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.

Objectives We evaluated the feasibility of using chemical shift gradient-echo (GE) in- and opposed-phase (IOP) imaging to grade glioma. Methods A phantom study was performed to investigate the correlation of 1 H MRS-visible lipids with the signal loss ratio (SLR) obtained using IOP imaging. A cross-sectional study approved by the institutional review board was carried out in 22 patients with different glioma grades. The patients underwent scanning using IOP imaging and single-voxel spectroscopy (SVS) using 3T MRI. The brain spectra acquisitions from solid and cystic components were obtained and correlated with the SLR for different grades. Results The phantom study showed a positive linear correlation between lipid quantification at 0.9 parts per million (ppm) and 1.3 ppm with SLR (r = 0.79–0.99, p < 0.05). In the clinical study, we found that SLR at the solid portions was the best measure for differentiating glioma grades using optimal cut-points of 0.064 and 0.086 with classification probabilities for grade II (S II  = 1), grade III (S III  = 0.50) and grade IV (S IV  = 0.89). Conclusions The results underscore the lipid quantification differences in grades of glioma and provide a more comprehensive characterization by using SLR in chemical shift GE IOP imaging. SLR in IOP sequence demonstrates good performance in glioma grading. Key Points • Strong correlation was seen between lipid concentration and SLR obtained using IOP • IOP sequence demonstrates significant differences in signal loss within the glioma grades • SLR at solid tumour portions was the best measure for differentiation • This sequence is applicable in a research capacity for glioma staging armamentarium

The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M–mutant; RELA fusion–positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma—a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.