Machine Learning for Better Prognostic Stratification and Driver Gene Identification Using Somatic Copy Number Variations in Anaplastic Oligodendroglioma

authors

• Rosenberg Shai
• Ducray Francois
• Alentorn Agusti
• Dehais Caroline
• Elarouci Nabila
• Kamoun Aurelie
• Marie Yannick
• Tanguy Marie-Laure
• de Reynies Aurélien
• Mokhtari Karima
• Figarella‑branger Dominique
• Delattre Jean-Yves
• Idbaih Ahmed

keywords

• Genomics
• Glioma
• Machine learning
• Oligodendroglioma
• Survival

abstract

1p/19q-codeleted anaplastic gliomas have variable clinical behavior. We have recently shown that the common 9p21.3 allelic loss is an independent prognostic factor in this tumor type. The aim of this study is to identify less frequent genomic copy number variations (CNVs) with clinical importance that may shed light on molecular oncogenesis of this tumor type.

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