Tracing TET1 expression in prostate cancer: discovery of malignant cells with a distinct oncogenic signature

Background!#!Ten-eleven translocation methylcytosine dioxygenase 1 (TET1) is involved in DNA demethylation and transcriptional regulation, plays a key role in the maintenance of stem cell pluripotency, and is dysregulated in malignant cells. The identification of cancer stem cells (CSCs) driving tumor growth and metastasis is the primary objective of biomarker discovery in aggressive prostate cancer (PCa). In this context, we analyzed TET1 expression in PCa.!##!Methods!#!A large-scale immunohistochemical analysis of TET1 was performed in normal prostate (NOR) and PCa using conventional slides (50 PCa specimens) and tissue microarrays (669 NOR and 1371 PCa tissue cores from 371 PCa specimens). Western blotting, RT-qPCR, and 450 K methylation array analyses were performed on PCa cell lines. Genome-wide correlation, gene regulatory network, and functional genomics studies were performed using publicly available data sources and bioinformatics tools.!##!Results!#!In NOR, TET1 was exclusively expressed in normal cytokeratin 903 (CK903)-positive basal cells. In PCa, TET1 was frequently detected in alpha-methylacyl-CoA racemase (AMACR)-positive tumor cell clusters and was detectable at all tumor stages and Gleason scores. Pearson's correlation analyses of PCa revealed 626 TET1-coactivated genes (r > 0.5) primarily encoding chromatin remodeling and mitotic factors. Moreover, signaling pathways regulating antiviral processes (62 zinc finger, ZNF, antiviral proteins) and the pluripotency of stem cells were activated. A significant proportion of detected genes exhibited TET1-correlated promoter hypomethylation. There were 161 genes encoding transcription factors (TFs), of which 133 were ZNF-TFs with promoter binding sites in TET1 and in the vast majority of TET1-coactivated genes.!##!Conclusions!#!TET1-expressing cells are an integral part of PCa and may represent CSCs with oncogenic potential

EphrinB2 repression through ZEB2 mediates tumour invasion and anti-angiogenic resistance

Diffuse invasion of the surrounding brain parenchyma is a major obstacle in the treatment of gliomas with various therapeutics, including anti-angiogenic agents. Here we identify the epi-/genetic and microenvironmental downregulation of ephrinB2 as a crucial step that promotes tumour invasion by abrogation of repulsive signals. We demonstrate that ephrinB2 is downregulated in human gliomas as a consequence of promoter hypermethylation and gene deletion. Consistently, genetic deletion of ephrinB2 in a murine high-grade glioma model increases invasion. Importantly, ephrinB2 gene silencing is complemented by a hypoxia-induced transcriptional repression. Mechanistically, hypoxia-inducible factor (HIF)-1α induces the EMT repressor ZEB2, which directly downregulates ephrinB2 through promoter binding to enhance tumour invasiveness. This mechanism is activated following anti-angiogenic treatment of gliomas and is efficiently blocked by disrupting ZEB2 activity. Taken together, our results identify ZEB2 as an attractive therapeutic target to inhibit tumour invasion and counteract tumour resistance mechanisms induced by anti-angiogenic treatment strategies.Deutsche ForschungsgemeinschaftBehring-Röntgen FoundationGutenberg Research CollegeJohannes Gutenberg University MainzDeutsche KrebshilfeGerman Ministry of Education and ResearchNational Genome NetworkBrain Tumor NetworkClusters of Excellence ‘Macromolecular Complexes'University Frankfurt‘Cardio-Pulmonary System (ECCPS)’Universities of Giessen and FrankfurtDepto. de Bioquímica y Biología MolecularFac. de FarmaciaTRUEpu

Deep learning reveals cancer metastasis and therapeutic antibody targeting in entire body.

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the preclinical stage