YAP/TAZ inhibition reduces metastatic potential of Ewing sarcoma cells

Ewing sarcoma (EwS) is a highly metastatic bone cancer characterized by the ETS fusion oncoprotein EWS-FLI1. EwS cells are phenotypically highly plastic and switch between functionally distinct cell states dependent on EWS-FLI1 fluctuations. Whereas EWS-FLI1high cells proliferate, EWS-FLI1low cells are migratory and invasive. Recently, we reported activation of MRTFB and TEAD, effectors of RhoA and Hippo signalling, upon low EWS-FLI1, orchestrating key steps of the EwS migratory gene expression program. TEAD and its co-activators YAP and TAZ are commonly overexpressed in cancer, providing attractive therapeutic targets. We find TAZ levels to increase in the migratory EWS-FLI1low state and to associate with adverse prognosis in EwS patients. We tested the effects of the potent YAP/TAZ/TEAD complex inhibitor verteporfin on EwS cell migration in vitro and on metastasis in vivo. Verteporfin suppressed expression of EWS-FLI1 regulated cytoskeletal genes involved in actin signalling to the extracellular matrix, effectively blocked F-actin and focal-adhesion assembly and inhibited EwS cell migration at submicromolar concentrations. In a mouse EwS xenograft model, verteporfin treatment reduced relapses at the surgical site and delayed lung metastasis. These data suggest that YAP/TAZ pathway inhibition may prevent EwS cell dissemination and metastasis, justifying further preclinical development of YAP/TAZ inhibitors for EwS treatment

Landscape of Bone Marrow Metastasis in Human Neuroblastoma Unraveled by Transcriptomics and Deep Multiplex Imaging

Bone marrow metastasis frequently occurs in patients with solid cancers and most often leads to poor outcome. Yet, the composition of bone marrow metastases, including tumor and surrounding cells, has so far not been characterized. Herein, we aimed to investigate the diversity of tumor and surrounding cells, i.e., the microenvironment, in bone marrow metastases, using the childhood tumor neuroblastoma as a model. To this end, we screened genome-wide datasets to define a panel of cell-specific markers for multiplex microscopy of metastatic bone marrow samples, and developed DeepFLEX, a computational pipeline for subsequent image analysis. Thereby, we identified 35,000 single cells covering metastasized tumor cells, and various types of developing immune and bone marrow cells. In parallel, we analyzed the transcriptome, i.e., all genes that are expressed as mRNA, of 38 patients with and without bone marrow metastasis. We found vast tumor cell diversity and identified a marker protein, FAIM2, which can help to identify a broader range of tumor cell variants. In addition we showed that tumor cell metastasis in the bone marrow is associated with an immune response resembling inflammation, and the presence of cells that can repress an immune attack against cancer cells. Our study suggests that metastatic tumor cells are shaping the bone marrow microenvironment and builds the basis to further investigate its clinical relevance

FEBS Letters / EWS-FLI1 impairs aryl hydrocarbon receptor activation by blocking tryptophan breakdown via the kynurenine pathway

Ewing sarcoma (ES) is an aggressive pediatric tumor driven by the fusion protein EWS-FLI1. We report that EWS-FLI1 suppresses TDO2-mediated tryptophan (TRP) breakdown in ES cells. Gene expression and metabolite analyses reveal an EWS-FLI1-dependent regulation of TRP metabolism. TRP consumption increased in the absence of EWS-FLI1, resulting in kynurenine and kynurenic acid accumulation, both aryl hydrocarbon receptor (AHR) ligands. Activated AHR binds to the promoter region of target genes. We demonstrate that EWS-FLI1 knockdown results in AHR nuclear translocation and activation. Our data suggest that EWS-FLI1 suppresses autocrine AHR signaling by inhibiting TDO2-catalyzed TRP breakdown.I 1225-B19(VLID)296348

An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization

Abstract Medical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potential, clinical embryologists routinely work with static images (morphological assessment) or short video sequences (time-lapse annotation). Recently, Artificial Intelligence models were utilized to support the embryo selection procedure. Even though they have proven their great potential in different in vitro fertilization settings, there is still considerable room for improvement. To support the advancement of algorithms in this research field, we built a dataset consisting of static blastocyst images and additional annotations. As such, Gardner criteria annotations, depicting a morphological blastocyst rating scheme, and collected clinical parameters are provided. The presented dataset is intended to be used to train deep learning models on static morphological images to predict Gardner’s criteria and clinical outcomes such as live birth. A benchmark of human expert’s performance in annotating Gardner criteria is provided