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Glioblastoma ranks among the most lethal of primary brain malignancies, with glioblastoma stem cells (GSCs) at the apex of tumor cellular hierarchies. Here, to discover novel therapeutic GSC targets, we interrogated gene expression profiles from GSCs, differentiated glioblastoma cells (DGCs), and neural stem cells (NSCs), revealing EYA2 as preferentially expressed by GSCs. Targeting EYA2 impaired GSC maintenance and induced cell cycle arrest, apoptosis, and loss of self-renewal. EYA2 displayed novel localization to centrosomes in GSCs, and EYA2 tyrosine (Tyr) phosphatase activity was essential for proper mitotic spindle assembly and survival of GSCs. Inhibition of the EYA2 Tyr phosphatase activity, via genetic or pharmacological means, mimicked EYA2 loss in GSCs in vitro and extended the survival of tumor-bearing mice. Supporting the clinical relevance of these findings, EYA2 portends poor patient prognosis in glioblastoma. Collectively, our data indicate that EYA2 phosphatase function plays selective critical roles in the growth and survival of GSCs, potentially offering a high therapeutic index for EYA2 inhibitors. 

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.