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SLIT2 is a secreted polypeptide that guides migration of cells expressing Roundabout 1 and 2 (ROBO1 and ROBO2) receptors. Herein, we investigated SLIT2/ROBO signaling effects in gliomas. In patients with glioblastoma (GBM), SLIT2 expression increased with malignant progression and correlated with poor survival and immunosuppression. Knockdown of SLIT2 in mouse glioma cells and patient-derived GBM xenografts reduced tumor growth and rendered tumors sensitive to immunotherapy. Tumor cell SLIT2 knockdown inhibited macrophage invasion and promoted a cytotoxic gene expression profile, which improved tumor vessel function and enhanced efficacy of chemotherapy and immunotherapy. Mechanistically, SLIT2 promoted microglia/macrophage chemotaxis and tumor-supportive polarization via ROBO1- and ROBO2-mediated PI3K-[gamma] activation. Macrophage Robo1 and Robo2 deletion and systemic SLIT2 trap delivery mimicked SLIT2 knockdown effects on tumor growth and the tumor microenvironment (TME), revealing SLIT2 signaling through macrophage ROBOs as a potentially novel regulator of the GBM microenvironment and immunotherapeutic target for brain tumors., Introduction Malignant gliomas are the most common primary brain tumors (1, 2). Among those, glioblastoma (GBM; WHO grade IV glioma) is the most frequent and aggressive tumor, accounting for more [...]

Lactate is a central metabolite in brain physiology but also contributes to tumor development. Glioblastoma (GB) is the most common and malignant primary brain tumor in adults, recognized by angiogenic and invasive growth, in addition to its altered metabolism. We show herein that lactate fuels GB anaplerosis by replenishing the tricarboxylic acid (TCA) cycle in absence of glucose. Lactate dehydrogenases (LDHA and LDHB), which we found spatially expressed in GB tissues, catalyze the interconversion of pyruvate and lactate. However, ablation of both LDH isoforms, but not only one, led to a reduction in tumor growth and an increase in mouse survival. Comparative transcriptomics and metabolomics revealed metabolic rewiring involving high oxidative phosphorylation (OXPHOS) in the LDHA/B KO group which sensitized tumors to cranial irradiation, thus improving mouse survival. When mice were treated with the antiepileptic drug stiripentol, which targets LDH activity, tumor growth decreased. Our findings unveil the complex metabolic network in which both LDHA and LDHB are integrated and show that the combined inhibition of LDHA and LDHB strongly sensitizes GB to therapy. Synopsis: This study highlights the importance of metabolic symbiosis dependent on lactate and lactate dehydrogenase isoforms (LDHA and B) in glioblastoma development. Inhibiting both lactate dehydrogenases may be a novel potential therapeutic approach for targeting glioblastoma. Lactate, which is produced in hypoxic environments, is secreted and taken up by oxidative cells to fuel the Krebs cycle to promote growth and invasion.Only double knockout of LDHA/B abolished lactate production, reduced tumor growth and invasion, and prolonged mouse survival.Tumors that no longer express LDH become more oxidative and more sensitive to radiation.The use of the LDH inhibitor stiripentol in clinical practice may be therapeutically relevant for glioblastoma. [ABSTRACT FROM AUTHOR]

Targeted sequencing is commonly used in clinical application of NGS technology since it enables generation of sufficient sequencing depth in the targeted genes of interest and thus ensures the best possible downstream analysis. This notwithstanding, the accurate discovery and annotation of disease causing mutations remains a challenging problem even in such favorable context. The difficulty is particularly salient in the case of third generation sequencing technology, such as PacBio. We present MICADo, a de Bruijn graph based method, implemented in python, that makes possible to distinguish between patient specific mutations and other alterations for targeted sequencing of a cohort of patients. MICADo analyses NGS reads for each sample within the context of the data of the whole cohort in order to capture the differences between specificities of the sample with respect to the cohort. MICADo is particularly suitable for sequencing data from highly heterogeneous samples, especially when it involves high rates of non-uniform sequencing errors. It was validated on PacBio sequencing datasets from several cohorts of patients. The comparison with two widely used available tools, namely VarScan and GATK, shows that MICADo is more accurate, especially when true mutations have frequencies close to backgound noise. The source code is available at http://github.com/cbib/MICADo. [ABSTRACT FROM AUTHOR]

Background: Glioblastomas are heterogeneous tumors composed of a necrotic and tumor core and an invasive periphery., Methods: Here, we performed a proteomics analysis of laser-capture micro-dissected glioblastoma core and invasive areas of patient-derived xenografts., Results: Bioinformatics analysis identified enriched proteins in central and invasive tumor areas. Novel markers of invasion were identified, the genes proteolipid protein 1 (PLP1) and Dynamin-1 (DNM1), which were subsequently validated in tumors and by functional assays., Conclusions: In summary, our results identify new networks and molecules that may play an important role in glioblastoma development and may constitute potential novel therapeutic targets., (© The Author(s) 2019. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Basal-like breast cancers are among the most aggressive cancers and effective targeted therapies are still missing. In order to identify new therapeutic targets, we performed Methyl-Seq and RNA-Seq of 10 breast cancer cell lines with different phenotypes. We confirmed that breast cancer subtypes cluster the RNA-Seq data but not the Methyl-Seq data. Basal-like tumor hypermethylated phenotype was not confirmed in our study but RNA-Seq analysis allowed to identify 77 genes significantly overexpressed in basal-like breast cancer cell lines. Among them, 48 were overexpressed in triple negative breast cancers of TCGA data. Some molecular functions were overrepresented in this candidate gene list. Genes involved in antioxydation, such as SOD1, MGST3 and PRDX or cadherin-binding genes, such as PFN1, ITGB1 and ANXA1, could thus be considered as basal like breast cancer biomarkers. We then sought if these genes were linked to BRCA1, since this gene is often inactivated in basal-like breast cancers. Nine genes were identified overexpressed in both basal-like breast cancer cells and BRCA1 mutated cells. Amongst them, at least 3 genes code for proteins implicated in epithelial cell migration and epithelial to mesenchymal transition (VIM, ITGB1 and RhoA). Our study provided several potential therapeutic targets for triple negative and BRCA1 mutated breast cancers. It seems that migration and mesenchymal properties acquisition of basal-like breast cancer cells is a key functional pathway in these tumors with a high metastatic potential., Competing Interests: Competing Interests: The authors have declared that no competing interest exists.