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Only a subset of recurrent glioblastoma (rGBM) responds to anti-PD-1 immunotherapy. Previously, we reported enrichment of BRAF/PTPN11 mutations in 30% of rGBM that responded to PD-1 blockade. Given that BRAF and PTPN11 promote MAPK/ERK signaling, we investigated whether activation of this pathway is associated with response to PD-1 inhibitors in rGBM, including patients that do not harbor BRAF/PTPN11 mutations. Here we show that immunohistochemistry for ERK1/2 phosphorylation (p-ERK), a marker of MAPK/ERK pathway activation, is predictive of overall survival following adjuvant PD-1 blockade in two independent rGBM patient cohorts. Single-cell RNA-sequencing and multiplex immunofluorescence analyses revealed that p-ERK was mainly localized in tumor cells and that high-p-ERK GBMs contained tumor-infiltrating myeloid cells and microglia with elevated expression of MHC class II and associated genes. These findings indicate that ERK1/2 activation in rGBM is predictive of response to PD-1 blockade and is associated with a distinct myeloid cell phenotype., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The evolutionary processes that drive universal therapeutic resistance in adult patients with diffuse glioma remain unclear 1,2 . Here we analysed temporally separated DNA-sequencing data and matched clinical annotation from 222 adult patients with glioma. By analysing mutations and copy numbers across the three major subtypes of diffuse glioma, we found that driver genes detected at the initial stage of disease were retained at recurrence, whereas there was little evidence of recurrence-specific gene alterations. Treatment with alkylating agents resulted in a hypermutator phenotype at different rates across the glioma subtypes, and hypermutation was not associated with differences in overall survival. Acquired aneuploidy was frequently detected in recurrent gliomas and was characterized by IDH mutation but without co-deletion of chromosome arms 1p/19q, and further converged with acquired alterations in the cell cycle and poor outcomes. The clonal architecture of each tumour remained similar over time, but the presence of subclonal selection was associated with decreased survival. Finally, there were no differences in the levels of immunoediting between initial and recurrent gliomas. Collectively, our results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner.

Spheroid cultures derived from explanted cancer specimens are an increasingly utilized resource for studying complex biological processes like tumor cell invasion and metastasis, representing an important bridge between the simplicity and practicality of 2-dimensional monolayer cultures and the complexity and realism of in vivo animal models. Temporal imaging of spheroids can capture the dynamics of cell behaviors and microenvironments, and when combined with quantitative image analysis methods, enables deep interrogation of biological mechanisms. This paper presents a comprehensive open-source software framework for Temporal Analysis of Spheroid Imaging (TASI) that allows investigators to objectively characterize spheroid growth and invasion dynamics. TASI performs spatiotemporal segmentation of spheroid cultures, extraction of features describing spheroid morpho-phenotypes, mathematical modeling of spheroid dynamics, and statistical comparisons of experimental conditions. We demonstrate the utility of this tool in an analysis of non-small cell lung cancer spheroids that exhibit variability in metastatic and proliferative behaviors.

Glioblastoma (GBM) contains diverse microenvironments with uneven distributions of oncogenic alterations and signaling networks. The diffusely infiltrative properties of GBM result in residual tumor at neurosurgical resection margins, representing the source of relapse in nearly all cases and suggesting that therapeutic efforts should be focused there. To identify signaling networks and potential druggable targets across tumor microenvironments (TMEs), we utilized 5-ALA fluorescence-guided neurosurgical resection and sampling, followed by proteomic analysis of specific TMEs. Reverse phase protein array (RPPA) was performed on 205 proteins isolated from the tumor margin, tumor bulk, and perinecrotic regions of 13 previously untreated, clinically-annotated and genetically-defined high grade gliomas. Differential protein and pathway signatures were established and then validated using western blotting, immunohistochemistry, and comparable TCGA RPPA datasets. We identified 37 proteins differentially expressed across high-grade glioma TMEs. We demonstrate that tumor margins were characterized by pro-survival and anti-apoptotic proteins, whereas perinecrotic regions were enriched for pro-coagulant and DNA damage response proteins. In both our patient cohort and TCGA cases, the data suggest that TMEs possess distinct protein expression profiles that are biologically and therapeutically relevant.

Whole-slide imaging of histologic sections captures tissue microenvironments and cytologic details in expansive high-resolution images. These images can be mined to extract quantitative features that describe tissues, yielding measurements for hundreds of millions of histologic objects. A central challenge in utilizing this data is enabling investigators to train and evaluate classification rules for identifying objects related to processes like angiogenesis or immune response. In this paper we describe HistomicsML, an interactive machine-learning system for digital pathology imaging datasets. This framework uses active learning to direct user feedback, making classifier training efficient and scalable in datasets containing 10 8 + histologic objects. We demonstrate how this system can be used to phenotype microvascular structures in gliomas to predict survival, and to explore the molecular pathways associated with these phenotypes. Our approach enables researchers to unlock phenotypic information from digital pathology datasets to investigate prognostic image biomarkers and genotype-phenotype associations.

Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.

Glioblastoma (GBM) is the most common primary brain tumor and has a dismal prognosis. Amplification of chromosome 12q13-q15 (Cyclin-dependent kinase 4 (CDK4) amplicon) is frequently observed in numerous human cancers including GBM. Phosphoinositide 3-kinase enhancer (PIKE) is a group of GTP-binding proteins that belong to the subgroup of centaurin GTPase family, encoded by CENTG1 located in CDK4 amplicon. However, the pathological significance of CDK4 amplicon in GBM formation remains incompletely understood. In the current study, we show that co-expression of PIKE-A and CDK4 in TP53/PTEN double knockout GBM mouse model additively shortens the latency of glioma onset and survival compared to overexpression of these genes alone. Consequently, p-mTOR, p-Akt and p-ERK pathways are highly upregulated in the brain tumors, in alignment with their oncogenic activities by CDK4 and PIKE-A stably transfected in GBM cell lines. Hence, our findings support that PIKE amplification or overexpression coordinately acts with CDK4 to drive GBM tumorigenesis.

The AMP-activated protein kinase, a key regulator of energy homeostasis, has a critical role in metabolic disorders and cancers. AMPK is mainly regulated by cellular AMP and phosphorylation by upstream kinases. Here, we show that PIKE-A binds to AMPK and blocks its tumor suppressive actions, which are mediated by tyrosine kinase Fyn. PIKE-A directly interacts with AMPK catalytic alpha subunit and impairs T172 phosphorylation, leading to repression of its kinase activity on the downstream targets. Mutation of Fyn phosphorylation sites on PIKE-A, depletion of Fyn, or pharmacological inhibition of Fyn blunts the association between PIKE-A and AMPK, resulting in loss of its inhibitory effect on AMPK. Cell proliferation and oncogenic assays demonstrate that PIKE-A antagonizes tumor suppressive actions of AMPK. In human glioblastoma samples, PIKE-A expression inversely correlates with the p-AMPK levels, supporting that PIKE-A negatively regulates AMPK activity in cancers. Thus, our findings provide additional layer of molecular regulation of the AMPK signaling pathway in cancer progression.

Merlin, encoded by the Neurofibromatosis 2 (NF2) gene, is a multifunctional tumor suppressor that integrates and regulates extracellular cues and intracellular signaling pathways, both at the plasma membrane and in the nucleus, to control cell proliferation, migration and invasion. Molecular mechanisms regulating merlin's tumor-suppressive activity have not been clearly defined. Here we report that merlin can be sumoylated on Lysine residue (K76) in vitro and in vivo. Sumoylation mediates merlin's intramolecular and intermolecular binding activities and regulates its cytoplasm/nucleus trafficking. Interestingly, sumoylation of merlin is regulated by its phosphorylation via Akt and PAK2 kinases. Mutation of K76 into arginine (R) abolishes its sumoylation, disrupts merlin cortical cytoskeleton residency and attenuates its stability. Using a K76R mutant merlin in a subcutaneous U87MG xenograft model, we demonstrate that merlin sumoylation is required for tumor-suppressive activity. Taken together, our findings indicate that merlin is sumoylated and that this post-translational modification is essential for tumor suppression.

Brain angiogenesis inhibitor 1 (BAI1) is a transmembrane protein with unknown function expressed primarily in normal but not tumoral brain. The finding of thrombospondin type 1 repeats in its extracellular domain suggested an antiangiogenic function, but the mechanisms by which a transmembrane receptor could inhibit angiogenesis remained unexplained. Here we demonstrate that BAI1 is proteolytically cleaved at a conserved G-protein-coupled receptor proteolytic cleavage site (GPS), releasing its 120 kDa extracellular domain. We named this secreted fragment Vasculostatin as it inhibited migration of endothelial cells in vitro and dramatically reduced in vivo angiogenesis. Both constitutive and doxycycline-induced expression of Vasculostatin elicited dose-dependent suppression of tumor growth and vascular density in mice, implicating Vasculostatin in the regulation of vascular homeostasis and tumor prevention. Generation of a soluble antiangiogenic factor by cleavage of a pre-existing transmembrane protein represents a novel mechanism for regulating vascular homeostasis and preventing tumorigenesis. Modulation of this cleavage or delivery of Vasculostatin may constitute novel treatment modalities for cancer and other diseases of aberrant angiogenesis, especially in the brain.