Similar Datasets
Project description:One-fifth of meningiomas classified as benign by World Health Organization (WHO) histopathological grading will behave malignantly. To better diagnose these tumors, several groups turned to DNA methylation, whereas we combined RNA-sequencing (RNA-seq) and cytogenetics. Both approaches were more accurate than histopathology in identifying aggressive tumors, but whether they revealed similar tumor types was unclear. We therefore performed unbiased DNA methylation, RNA-seq, and cytogenetic profiling on 110 primary meningiomas WHO grade I and II). Each technique distinguished the same three groups (two benign and one malignant) as our previous molecular classification; integrating these methods into one classifier further improved accuracy. Computational modeling revealed strong correlations between transcription and cytogenetic changes, particularly loss of chromosome 1p, in malignant tumors. Applying our classifier to data from previous studies also resolved certain anomalies entailed by grouping tumors by WHO grade. Accurate classification will therefore elucidate meningioma biology as well as improve diagnosis and prognosis.
| S-EPMC11313601 | biostudies-literature
Project description:Purpose: Clustering of meningiomas using DNA methylation and RNA-sequencing data yields groups which predict clinical behavior than the current clinical standard of histopathologic grading. Both techniques segregate many common genomic features seen in meningiomas similarly, raising the question of whether they were identifying the same underyling groups. Methods: DNA methylation profiling (EPIC 850K array) and RNA-sequencing of 110 primary meningiomas was performed. Unsupervised clustering was performed using each type of data, followed by computational modeling which explored correlations of promoter methylation and copy number variation (CNV) with gene expression. Results: We performed unsupervised clustering of the DNA methylation data and gound three clusters to be optimal. These clusters aligned closely with our prior transcriptional classification (Patel et al. PNAS 2019). These groups also closely aligned with groups defined by large-scale cytogenetic changes and merlin expression/genomic instability, suggesting the presence of three biologic groups of meningioma that can be identified by multiple methods. Computational modeling demonstrated that both promoter methylation and CNV correlated closely with gene expression differences seen in meningioma and our biologic groups, although cytogenetic changes (particularly chr 1p loss) was predominant in the clinically aggressive tumors. Conclusions: Our anaylsis suggests the presence of three biologic groups of meningioma (MenG A, B, and C) which can be accurately identified through DNA methylation, RNA-seq, and cytogenetic profiling.
2021-11-30 | GSE189672 | GEO
Project description:Purpose: Clustering of meningiomas using DNA methylation and RNA-sequencing data yields groups which predict clinical behavior than the current clinical standard of histopathologic grading. Both techniques segregate many common genomic features seen in meningiomas similarly, raising the question of whether they were identifying the same underyling groups. Methods: DNA methylation profiling (EPIC 850K array) and RNA-sequencing of 110 primary meningiomas was performed. Unsupervised clustering was performed using each type of data, followed by computational modeling which explored correlations of promoter methylation and copy number variation (CNV) with gene expression. Results: We performed unsupervised clustering of the DNA methylation data and gound three clusters to be optimal. These clusters aligned closely with our prior transcriptional classification (Patel et al. PNAS 2019). These groups also closely aligned with groups defined by large-scale cytogenetic changes and merlin expression/genomic instability, suggesting the presence of three biologic groups of meningioma that can be identified by multiple methods. Computational modeling demonstrated that both promoter methylation and CNV correlated closely with gene expression differences seen in meningioma and our biologic groups, although cytogenetic changes (particularly chr 1p loss) was predominant in the clinically aggressive tumors. Conclusions: Our analysis suggests the presence of three biologic groups of meningioma (MenG A, B, and C) which can be accurately identified through DNA methylation, RNA-seq, and cytogenetic profiling.
2021-11-30 | GSE189521 | GEO
Project description:Multiple approaches converge on three biological subtypes of meningioma and extract new insights from published studies
| PRJNA784115 | ENA
Project description:Multiple approaches converge on three biological subtypes of meningioma and extract new insights from published studies [Methylation array]
| PRJNA783320 | ENA
Project description:Multiple approaches converge on three biological subtypes of meningioma and extract new insights from published studies [RNA-Seq]
| PRJNA784116 | ENA
Project description:Glioblastoma (WHO grade IV astrocytoma) is the most frequent primary brain tumor in adults, representing a highly heterogeneous group of neoplasms that are among the most aggressive and challenging cancers to treat. An improved understanding of the molecular pathways that drive malignancy in glioblastoma has led to the development of various biomarkers and the evaluation of several agents specifically targeting tumor cells and the tumor microenvironment. A number of rational approaches are being investigated, including therapies targeting tumor growth factor receptors and downstream pathways, cell cycle and epigenetic regulation, angiogenesis and antitumor immune response. Moreover, recent identification and validation of prognostic and predictive biomarkers have allowed implementation of modern trial designs based on matching molecular features of tumors to targeted therapeutics. However, while occasional targeted therapy responses have been documented in patients, to date no targeted therapy has been formally validated as effective in clinical trials. The lack of knowledge about relevant molecular drivers in vivo combined with a lack of highly bioactive and brain penetrant-targeted therapies remain significant challenges. In this article, we review the most promising biological insights that have opened the way for the development of targeted therapies in glioblastoma, and examine recent data from clinical trials evaluating targeted therapies and immunotherapies. We discuss challenges and opportunities for the development of these agents in glioblastoma.
| S-EPMC5834086 | biostudies-literature
Project description:It has been proposed that night shift work could increase breast cancer incidence. A 2007 World Health Organization review concluded, mainly from animal evidence, that shift work involving circadian disruption is probably carcinogenic to humans. We therefore aimed to generate prospective epidemiological evidence on night shift work and breast cancer incidence.Overall, 522 246 Million Women Study, 22 559 EPIC-Oxford, and 251 045 UK Biobank participants answered questions on shift work and were followed for incident cancer. Cox regression yielded multivariable-adjusted breast cancer incidence rate ratios (RRs) and 95% confidence intervals (CIs) for night shift work vs no night shift work, and likelihood ratio tests for interaction were used to assess heterogeneity. Our meta-analyses combined these and relative risks from the seven previously published prospective studies (1.4 million women in total), using inverse-variance weighted averages of the study-specific log RRs.In the Million Women Study, EPIC-Oxford, and UK Biobank, respectively, 673, 28, and 67 women who reported night shift work developed breast cancer, and the RRs for any vs no night shift work were 1.00 (95% CI?=?0.92 to 1.08), 1.07 (95% CI?=?0.71 to 1.62), and 0.78 (95% CI?=?0.61 to 1.00). In the Million Women Study, the RR for 20 or more years of night shift work was 1.00 (95% CI?=?0.81 to 1.23), with no statistically significant heterogeneity by sleep patterns or breast cancer risk factors. Our meta-analysis of all 10 prospective studies included 4660 breast cancers in women reporting night shift work; compared with other women, the combined relative risks were 0.99 (95% CI?=?0.95 to 1.03) for any night shift work, 1.01 (95% CI?=?0.93 to 1.10) for 20 or more years of night shift work, and 1.00 (95% CI?=?0.87 to 1.14) for 30 or more years.The totality of the prospective evidence shows that night shift work, including long-term shift work, has little or no effect on breast cancer incidence.
| S-EPMC5241898 | biostudies-other
Project description:Nature features a plethora of extraordinary photonic architectures that have been optimized through natural evolution in order to more efficiently reflect, absorb or scatter light. While numerical optimization is increasingly and successfully used in photonics, it has yet to replicate any of these complex naturally occurring structures. Using evolutionary algorithms inspired by natural evolution and performing particular optimizations (maximize reflection for a given wavelength, for a broad range of wavelength or maximize the scattering of light), we have retrieved the most stereotypical natural photonic structures. Whether those structures are Bragg mirrors, chirped dielectric mirrors or the gratings on top of Morpho butterfly wings, our results indicate how such regular structures might have spontaneously emerged in nature and to which precise optical or fabrication constraints they respond. Comparing algorithms show that recombination between individuals, inspired by sexual reproduction, confers a clear advantage that can be linked to the fact that photonic structures are fundamentally modular: each part of the structure has a role which can be understood almost independently from the rest. Such an in silico evolution also suggests original and elegant solutions to practical problems, as illustrated by the design of counter-intuitive anti-reflective coatings for solar cells.
| S-EPMC7374560 | biostudies-literature
Project description:BackgroundWHO grade 2 meningiomas, including atypical, chordoid, and clear cell subtypes, form a heterogenous group of meningiomas with varying aggressiveness and clinical behavior.ObjectiveTo demonstrate the differences of clinical-histopathological characteristics and long-term outcomes among these 3 subtypes.MethodsA total of 609 consecutive patients diagnosed with WHO grade 2 meningiomas (543 atypical meningiomas [AMs], 36 chordoid meningiomas [CMs], and 30 clear cell meningiomas [CCMs]) from 2010 to 2018 were enrolled in this study. We compared the clinical-histopathological characteristics and long-term outcomes in these 3 subtypes and assessed survival differences among the subtypes. Targeted panel sequencing of meningioma-relevant genes was performed in the cases of CM.ResultsThe patients with CCM were significantly younger than those with AM ( P < .001) and CM ( P = .016). CMs were more likely to receive gross total resection than AMs and CCMs ( P = .033). The Ki-67 index was lower ( P < .001) while the progesterone receptors-positive rate was higher ( P = .034) in CM than in AM and CCM. Importantly, survival analysis demonstrated that CM had better progression-free survival ( P = .022) and overall survival ( P = .0056) than non-CM tumors. However, the PFS of CM was still worse than WHO grade 1 meningiomas ( P < .001). Alterations in NF2 (20.6%) and KMT2C (26.5%) were associated with poorer PFS in CM ( P = .013 for NF2 ; P = .021 for KMT2C ).ConclusionPatients with CM had better long-term postoperative outcomes than the other WHO grade 2 subtypes. A lower Ki-67 index, higher PR status, higher extent of resection, and lower frequency of NF2 alteration might contribute to favorable clinical outcomes of CM.
| S-EPMC9988284 | biostudies-literature