2022
DOI: 10.1016/j.jmoldx.2022.04.009
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Validation of Whole Genome Methylation Profiling Classifier for Central Nervous System Tumors

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Cited by 14 publications
(12 citation statements)
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“…The development of the DKFZ/Heidelberg CNS tumor methylation classifier significantly contributes to our comprehension of methylation patterns and their interplay with clinical variables. This advancement greatly enhances the precision of molecular pathological classification in the realm of brain tumors [ 42 , 44 ].…”
Section: Resultsmentioning
confidence: 99%
“…The development of the DKFZ/Heidelberg CNS tumor methylation classifier significantly contributes to our comprehension of methylation patterns and their interplay with clinical variables. This advancement greatly enhances the precision of molecular pathological classification in the realm of brain tumors [ 42 , 44 ].…”
Section: Resultsmentioning
confidence: 99%
“…Previous studies of methylation classifiers have indicated that sample tumor purities affect classifier performance 17,27,28 . Tumor purities in the present study were similar to those used for sarcoma classification 28 , but much lower than purities seen in central nervous system tumor classification 17,27 . While low tumor purities resulted in lower TUO classifier scores, they did not have a substantial effect on classification accuracy when not including classifier scores, similar to the patterns seen in previous sarcoma and TUO classifiers 28,29 .…”
Section: Discussionmentioning
confidence: 99%
“…A lack of class annotation has limited the value of investigations in many cases. Unsupervised clustering methods coupled with highly annotated clinical samples leads to improved model prediction and has been harnessed to successfully create novel classifiers for CNS tumors and sarcomas 17,27,28 . While most TUO classifiers to date have used data from TCGA and Gene Expression Omnibus (GEO), few approaches have been clinically validated using institutional TUO samples to assess real-world utility.…”
Section: Introductionmentioning
confidence: 99%
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“…Retrospective analyses of large CNS cancer cohorts have demonstrated the robustness of methylation-based classifier approaches [ 2 , 3 ] and their ability to provide more precise diagnostic information in the setting of indeterminant diagnoses, to the extent that the 2021 WHO Guidelines for diagnosis of CNS malignancies included methylation-based classification within the standard of diagnosis [ 4 ]. Recently, a large prospective trial of methylation array-based classification was reported for pediatric patients with CNS malignancies, further demonstrating improved precision in sub-group classification (e.g., higher granularity among similarly subtyped classes of CNS cancers) and linking new sub-group classifications to outcomes [ 5 ▪▪ ].…”
Section: Overview Of Genomic Toolsmentioning
confidence: 99%