Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory

Targeting integrated epigenetic and metabolic pathways in lethal childhood PFA ependymomas

Childhood posterior fossa group A ependymomas (PFAs) have limited treatment options and bear dismal prognoses compared to group B ependymomas (PFBs). PFAs overexpress the oncohistone-like protein EZHIP (enhancer of Zeste homologs inhibitory protein), causing global reduction of repressive histone H3 lysine 27 trimethylation (H3K27me3), similar to the oncohistone H3K27M. Integrated metabolic analyses in patient-derived cells and tumors, single-cell RNA sequencing of tumors, and noninvasive metabolic imaging in patients demonstrated enhanced glycolysis and tricarboxylic acid (TCA) cycle metabolism in PFAs. Furthermore, high glycolytic gene expression in PFAs was associated with a poor outcome. PFAs demonstrated high EZHIP expression associated with poor prognosis and elevated activating mark histone H3 lysine 27 acetylation (H3K27ac). Genomic H3K27ac was enriched in PFAs at key glycolytic and TCA cycle–related genes including hexokinase-2 and pyruvate dehydrogenase. Similarly, mouse neuronal stem cells (NSCs) expressing wild-type EZHIP (EZHIP-WT) versus catalytically attenuated EZHIP-M406K demonstrated H3K27ac enrichment at hexokinase-2 and pyruvate dehydrogenase, accompanied by enhanced glycolysis and TCA cycle metabolism. AMPKα-2, a key component of the metabolic regulator AMP-activated protein kinase (AMPK), also showed H3K27ac enrichment in PFAs and EZHIP-WT NSCs. The AMPK activator metformin lowered EZHIP protein concentrations, increased H3K27me3, suppressed TCA cycle metabolism, and showed therapeutic efficacy in vitro and in vivo in patient-derived PFA xenografts in mice. Our data indicate that PFAs and EZHIP-WT–expressing NSCs are characterized by enhanced glycolysis and TCA cycle metabolism. Repurposing the antidiabetic drug metformin lowered pathogenic EZHIP, increased H3K27me3, and suppressed tumor growth, suggesting that targeting integrated metabolic/epigenetic pathways is a potential therapeutic strategy for treating childhood ependymomas