Lauber C, et al. - Researchers used well-established computational approaches (unsupervised clustering, statistical testing, network inference) to analyze gene copy number, mutation, and expression data of 193 histologically classified oligodendrogliomas from The Cancer Genome Atlas (TCGA). Sufficiency of gene copy number profiles alone to derive molecular subgroups of histologically classified oligodendrogliomas, that are well-embedded into general glioma classification schemes, was shown. Moreover, novel putative major regulators and characteristic stemness signatures were identified, which were suggestive of possible activity of different developmental programs in these subgroups.