Glioblastomas exhibit a hierarchical cellular organization, suggesting that they are driven by neoplastic stem cells that retain partial yet abnormal differentiation potential. Here, we show that a large subset of patient-derived glioblastoma stem cells (GSCs) express high levels of Achaete-scute homolog 1 (ASCL1), a proneural transcription factor involved in normal neurogenesis. ASCL1hi GSCs exhibit a latent capacity for terminal neuronal differentiation in response to inhibition of Notch signaling, whereas ASCL1lo GSCs do not. Increasing ASCL1 levels in ASCL1lo GSCs restores neuronal lineage potential, promotes terminal differentiation, and attenuates tumorigenicity. ASCL1 mediates these effects by functioning as a pioneer factor at closed chromatin, opening new sites to activate a neurogenic gene expression program. Directing GSCs toward terminal differentiation may provide therapeutic applications for a subset of GBM patients and strongly supports efforts to restore differentiation potential in GBM and other cancers.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows has enabled clinically-relevant tumor type predictions to guide treatment decisions in real time.

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