Advanced molecular neuropathology to increase diagnostic accuracy in pediatric neurooncology
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001006680 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:To assess their utility in routine neuropathology, we prospectively integrated DNA methylation-based CNS tumor classification and targeted gene panel sequencing of tumor and constitutional DNA with blinded neuropathological reference diagnostics for a population-based cohort of > 1,200 newly-diagnosed pediatric patients.
2023-01-30 | GSE215240 | GEO
Project description:Cavitating ultrasonic aspirator devices are frequently used in pediatric neurosurgery for efficient microsurgical resection of brain tumours while minimizing tissue damage to surrounding healthy brain. Within this study molecular diagnostics using methylation-based classification of ultrasonic aspirated samples was performed and performance against routine microarray diagnostics was assessed.
2024-02-08 | GSE255044 | GEO
Project description:Background: Modern neuropathology is challenged by an increasing number of clinically-relevant CNS tumor subgroups that require assessment of a multitude of molecular markers for classification, as well a highly trained medical staff. Failure to meet this challenge leads to tumor misclassification, which can have severe consequences for affected patients. Methods: We compiled a cohort of genome-wide DNA methylation profiles of 2,682 tumors from 82 histologically and/or molecularly distinct CNS tumor classes across all ages and histologies that served as reference for a Random Forest-based diagnostic classifier. This classifier was used to prospectively investigate a further 1,104 CNS tumor samples in order to determine its clinical utility. Results: The classifier was able to reliably assign tumor samples to a given diagnostic category with a misclassification rate of less than 2%. The system functioned robustly across laboratories and using different DNA methylation profiling techniques. Prospective application to clinical samples resulted in a reclassification of 12% of tumors compared with standard practice alone. A further 12% could not be classified by methylation profiling – this subset was highly enriched for unusual syndrome-associated tumors and likely novel entities. Conclusion: This study represents a proof-of-concept for the application of machine learning approaches in molecular diagnostics using a single, easy-to-use assay. The reference cohort and Random Forest-based classifier are available online as a valuable community tool for improving precision in brain tumor diagnostics. We expect that approaches similar to the one presented herein will rapidly restructure diagnostic practice in neurooncology and across tumor pathology.
2018-01-18 | GSE109379 | GEO
Project description:Background: Modern neuropathology is challenged by an increasing number of clinically-relevant CNS tumor subgroups that require assessment of a multitude of molecular markers for classification, as well a highly trained medical staff. Failure to meet this challenge leads to tumor misclassification, which can have severe consequences for affected patients. Methods: We compiled a cohort of genome-wide DNA methylation profiles of 2,682 tumors from 82 histologically and/or molecularly distinct CNS tumor classes across all ages and histologies that served as reference for a Random Forest-based diagnostic classifier. This classifier was used to prospectively investigate a further 1,104 CNS tumor samples in order to determine its clinical utility. Results: The classifier was able to reliably assign tumor samples to a given diagnostic category with a misclassification rate of less than 2%. The system functioned robustly across laboratories and using different DNA methylation profiling techniques. Prospective application to clinical samples resulted in a reclassification of 12% of tumors compared with standard practice alone. A further 12% could not be classified by methylation profiling – this subset was highly enriched for unusual syndrome-associated tumors and likely novel entities. Conclusion: This study represents a proof-of-concept for the application of machine learning approaches in molecular diagnostics using a single, easy-to-use assay. The reference cohort and Random Forest-based classifier are available online as a valuable community tool for improving precision in brain tumor diagnostics. We expect that approaches similar to the one presented herein will rapidly restructure diagnostic practice in neurooncology and across tumor pathology.
2018-01-14 | GSE90496 | GEO
Project description:sPNETs are highly malignant embryonal brain tumours of poor prognosis. The underlying biology is poorly understood. To address this we therefore performed high resolution genetic analysis. 36 CNS PNETs and 8 PBs were analysed using the Affymetrix 100K and 500K Mapping Set to identify copy number imbalance at both the chromosome and gene level. Keywords: Affymetrix 100K SNP array, Affymetrix 500K SNP arrays 36 CNS PNETs and 8 PBs with constitutional controls
2011-08-21 | E-GEOD-12370 | biostudies-arrayexpress
Project description:Products of conception that were diagnosed either euploid or aneuploid during routine cytogenetic analysis were analysed by a novel CGH+SNP array for the evaluation of its usefulness in clinical diagnostics.
2014-01-23 | E-MTAB-1725 | biostudies-arrayexpress
Project description:Klebsiella pneumoniae complex strains isolated in routine diagnostics
| PRJEB49326 | ENA
Project description:Individualized diagnosis prediction classifiers were successfully constructed through expression profiling of a total of 8,644 genes in 49 patients with relapse/refractory diffuse large B cell lymphoma, prospectively treated in a randomized trial. keyword(s): Diagnosis prediction All lymphoma cases were predicted as belonging either to germical center B-cell like DLBCL or activated B cell like DLBCL based on the expression of a set of 54 genes (140 probes).
2011-09-28 | E-GEOD-26812 | biostudies-arrayexpress
Project description:89 tumors from women that were eligible for, and subjected to, routine diagnostic testing according to the HBOC criteria but were negative for pathogenic BRCA1/2-mutations or carried an UV in either BRCA1/2 A BRCA2-classifier was built using array-CGH profiles of 28 BRCA2-mutated and 28 sporadic breast tumors. The classifier was validated on an independent group of 19 BRCA2-mutated and 19 sporadic breast tumors. Subsequently, we tested 89 breast tumors from suspected hereditary breast (and ovarian) cancer (HBOC) families, in which either no BRCA1/2 mutation or an UV had been found by routine diagnostics.
2010-07-05 | E-GEOD-22481 | biostudies-arrayexpress
Project description:Background. Gliomas, especially the high-grade glioblastomas (GBM), are highly aggressive tumors in the central nervous system (CNS) with dismal clinical outcomes. Effective biomarkers, which are not currently available, may improve clinical outcomes through early detection. We sought to develop a non-invasive diagnostic approach for gliomas based on 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA). Methods. We obtained genome-wide 5hmC profiles using the 5hmC-Seal technique in cfDNA samples from 111 prospectively enrolled patients with gliomas and 111 age-, gender-matched healthy individuals, which were split into a training set and a validation set. Integrated models comprised of 5hmC levels summarized for gene bodies, long non-coding RNAs (lncRNAs), cis-regulatory elements, and repetitive elements were developed using the elastic net regularization under a case-control design. Results. The integrated 5hmC-based models differentiated healthy individuals from gliomas (AUC [area under the curve] = 84%; 95% confidence interval [CI], 74-93%), GBM patients (AUC = 84%; 95% CI, 74-94%), WHO II-III glioma patients (AUC = 86%; 95% CI, 76-96%), regardless of IDH1 (encoding isocitrate dehydrogenase) mutation status or other glioma-related pathological features such as TERT, TP53 in the validation set. Furthermore, the 5hmC biomarkers in cfDNA showed the potential as an independent indicator from IDH1 mutation status and worked in synergy with IDH1 mutation to distinguish GBM from WHO II-III gliomas. Exploration of the 5hmC biomarkers for gliomas revealed relevance to glioma biology. Conclusions. The 5hmC-Seal in cfDNA offers the promise as a non-invasive approach for effective detection of gliomas in a screening program.
2021-05-30 | GSE132118 | GEO