Prospective genomically guided identification of "early/evolving" and "undersampled" IDH-wildtype glioblastoma leads to improved clinical outcomes.

Yalan ZhangCalixto-Hope G LucasJacob S YoungRamin A MorshedLucie McCoyNancy Ann Oberheim BushJennie W TaylorMariza DarasNicholas A ButowskiJavier E Villanueva-MeyerSoonmee ChaMargaret WrenschJohn K WienckeJulieann C LeeMelike PekmezciJoanna J PhillipsArie PerryAndrew W BollenManish K AghiPhilip TheodosopoulosEdward F ChangShawn L Hervey-JumperMitchel S BergerJennifer L ClarkeSusan M ChangAnnette M MolinaroDavid A Solomon

Published in: Neuro-oncology (2022)

These results support routine use of genomic and/or epigenomic profiling to accurately classify glial neoplasms, as these assays not only improve diagnostic classification but critically lead to more appropriate patient management that can improve clinical outcomes.

Keyphrases

machine learning
deep learning
case report
clinical practice
single cell
copy number
gene expression
spinal cord
wild type
genome wide
bioinformatics analysis