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Background: Epidemiological studies suggest that metformin reduces the risk of developing several types of cancer, including gliomas, and improves the overall survival in cancer patients. Nevertheless, while the effect of metformin on cancer cells has been extensively studied, its impact on other components of the tumour microenvironment, such as macrophages, is less understood. Results: Metformin-treated mouse bone marrow cells differentiate into spindle-shaped macrophages exhibiting increased phagocytic activity and tumour cell cytotoxicity coupled with modulated expression of co-stimulatory molecules displaying reduced sensitivity to inflammatory cues compared with untreated cells. Transcriptional analyses of metformin-treated mouse bone marrow-derived macrophages show decreased expression levels of pro-tumour genes, including Tgfbi and Il1β, related to enhanced mTOR/HIF1α signalling and metabolic rewiring towards glycolysis. Significance: Our study provides novel insights into the immunomodulatory properties of metformin in macrophages and its potential application in preventing tumour onset and in cancer immunotherapy.

We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist's efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas; thus, providing insights to pathologists concerning the most informative parts of the WSI.

Summary: Treatment options for patients with NRAS-mutant melanoma are limited and lack an efficient targeted drug combination that significantly increases overall and progression-free survival. In addition, targeted therapy success is hampered by the inevitable emergence of drug resistance. A thorough understanding of the molecular processes driving cancer cells’ escape mechanisms is crucial to tailor more efficient follow-up therapies. We performed single-cell RNA sequencing of NRAS-mutant melanoma treated with MEK1/2 plus CDK4/6 inhibitors to decipher transcriptional transitions during the development of drug resistance. Cell lines resuming full proliferation (FACs [fast-adapting cells]) and cells that became senescent (SACs [slow-adapting cells]) over prolonged treatment were identified. The early drug response was characterized by transitional states involving increased ion signaling, driven by upregulation of the ATP-gated ion channel P2RX7. P2RX7 activation was associated with improved therapy responses and, in combination with targeted drugs, could contribute to the delayed onset of acquired resistance in NRAS-mutant melanoma.

Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.

Summary: C/EBPα represents a paradigm intrinsically disordered transcription factor containing short linear motifs and post-translational modifications (PTM). Unraveling C/EBPα protein interaction networks is a prerequisite for understanding the multi-modal functions of C/EBPα in hematopoiesis and leukemia. Here, we combined arrayed peptide matrix screening (PRISMA) with BioID to generate an in vivo validated and isoform specific interaction map of C/EBPα. The myeloid C/EBPα interactome comprises promiscuous and PTM-regulated interactions with protein machineries involved in gene expression, epigenetics, genome organization, DNA replication, RNA processing, and nuclear transport. C/EBPα interaction hotspots coincide with homologous conserved regions of the C/EBP family that also score as molecular recognition features. PTMs alter the interaction spectrum of C/EBP-motifs to configure a multi-valent transcription factor hub that interacts with multiple co-regulatory components, including BAF/SWI-SNF or Mediator complexes. Combining PRISMA and BioID is a powerful strategy to systematically explore the PTM-regulated interactomes of intrinsically disordered transcription factors.

The present data are related to the article entitled “Insights into ligand stimulation effects on gastro-intestinal stromal tumors signaling” (C. Bahlawane, M. Schmitz, E. Letellier, K. Arumugam, N. Nicot, P.V. Nazarov, S. Haan, 2016) [1]. Constitutive and ligand-derived signaling pathways mediated by KIT and PDGFRA mutated proteins found in gastrointestinal stromal tumors (GIST) were compared. Expression of mutant proteins was induced by doxycycline in an isogenic background (Hek293 cells). Kit was identified by FACS at the cell surface and found to be quickly degraded or internalized upon SCF stimulation for both Kit Wild type and Kit mutant counterparts. Investigation of the main activated pathways in GIST unraveled a new feature specific for oncogenic KIT mutants, namely their ability to be further activated by Kit ligand, the stem cell factor (scf). We were also able to identify the MAPK pathway as the most prominent target for a common inhibition of PDGFRA and KIT oncogenic signaling. Western blotting and micro-array analysis were applied to analyze the capacities of the mutant to induce an effective STATs response. Among all Kit mutants, only Kit Ex11 deletion mutant was able to elicit an effective STATs response whereas all PDGFRA were able to do so.

Long non-coding RNAs (lncRNAs) have recently emerged as a novel group of non-coding RNAs able to regulate gene expression. While their role in cardiac disease is only starting to be understood, their involvement in cardiac hypertrophy is poorly known. We studied the association between lncRNAs and left ventricular hypertrophy using whole transcriptome microarrays. Wild-type mice and mice overexpressing the adenosine A2A receptor were subjected to transverse aortic constriction (TAC) to induce left ventricular hypertrophy. Expression profiles of lncRNAs in the heart were characterized using genome-wide microarrays. An analytical pipeline was specifically developed to extract lncRNA data from microarrays. We identified 2 lncRNAs up-regulated and 3 lncRNAs down-regulated in the hearts of A2A-receptor overexpressing-mice subjected to TAC compared to wild-type mice. Differential expression of these 2 lncRNAs was validated by quantitative PCR. Complete microarray dataset is available at Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE45423. Here, we describe in details the experimental design, microarray performance and analysis.