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Background The IL-36 pathway plays a key role in the pathogenesis of generalized pustular psoriasis (GPP). In a proof-of-concept clinical trial, treatment with spesolimab, an anti-IL-36 receptor antibody, resulted in rapid skin and pustular clearance in patients presenting with GPP flares. Objective To compare the molecular profiles of lesional and non-lesional skin from patients with GPP or palmoplantar pustulosis (PPP) with skin from healthy volunteers, and to investigate the molecular changes after spesolimab treatment in the skin and blood of patients with GPP flares. Methods Pre- and post-treatment skin and blood samples were collected from patients with GPP who participated in a single-arm, Phase I study (n = 7). Skin biopsies from patients with PPP (n = 8) and healthy volunteers (n = 16) were obtained for comparison at baseline. Biomarkers were assessed by RNA sequencing, histopathology and immunohistochemistry. Results In GPP and PPP lesions, 1287 transcripts were commonly up- or downregulated. Selected transcripts from the IL-36 signalling pathway were upregulated in untreated GPP and PPP lesions. In patients with GPP, IL-36 pathway-related signatures, T helper (Th)1/Th17 and innate inflammation signalling, neutrophilic mediators and keratinocyte-driven inflammation pathways, were downregulated by spesolimab as early as Week 1. Spesolimab also decreased related serum biomarkers and cell populations in the skin lesions from patients with GPP, including CD3+ T, CD11c+, IL-36γ+ cells and lipocalin-2-expressing cells. Conclusion In patients with GPP, spesolimab showed rapid modulation of commonly dysregulated molecular pathways in GPP and PPP, which may be associated with improved clinical outcomes.

The simultaneous measurement of thousands of genes gives the opportunity to personalize and improve cancer therapy. In addition, the integration of meta-data such as protein-protein interaction (PPI) information into the analyses helps in the identification and prioritization of genes from these screens. Here, we describe a computational approach that identifies genes prognostic for outcome by combining gene profiling data from any source with a network of known relationships between genes.

During the last years the technology used for gene expression analysis has changed dramatically. The old mainstay, DNA microarray, has served its due course and will soon be replaced by next-generation sequencing (NGS), the Swiss army knife of modern high-throughput nucleic acid-based analysis. Therefore preparation technologies have to adapt to suit the emerging NGS technology platform. Moreover, interpretation of the results is still time consuming and employs the use of high-end computers usually not found in molecular biology laboratories. Alternatively, cloud computing might solve this problem. Nevertheless, these new challenges have to be embraced for gene expression analysis in general.

During the last years the technology used for gene expression analysis has changed dramatically. The old mainstay, DNA microarray, has served its due course and will soon be replaced by next-generation sequencing (NGS), the Swiss army knife of modern high-throughput nucleic acid-based analysis. Therefore preparation technologies have to adapt to suit the emerging NGS technology platform. Moreover, interpretation of the results is still time consuming and employs the use of high-end computers usually not found in molecular biology laboratories. Alternatively, cloud computing might solve this problem. Nevertheless, these new challenges have to be embraced for gene expression analysis in general.

The simultaneous measurement of thousands of genes gives the opportunity to personalize and improve cancer therapy. In addition, the integration of meta-data such as protein-protein interaction (PPI) information into the analyses helps in the identification and prioritization of genes from these screens. Here, we describe a computational approach that identifies genes prognostic for outcome by combining gene profiling data from any source with a network of known relationships between genes.

Applications in the biomedical sector have a rising demand for computational power due to the growing amount of biological data. However, the setup and maintenance of a sufficient computational infrastructure is costly. Cloud computing allows distributed computing over a network and maximizes the effectiveness of the shared resources. Nevertheless, security concerns rise when valuable research data is transferred to a public Cloud. Herein, we describe – from the application and security perspective – three biomedical case studies from different domains: Patent annotation, cancer outcome prediction, and drug target prediction. We elaborate on different strategies to secure the data and results in the Cloud as well as on the infrastructure needed. The proposed Cloud solutions could help to adapt other similar algorithms from different domains to benefit from Cloud computing.

Applications in the biomedical sector have a rising demand for computational power due to the rapid growth of biological data. However, the setup and maintenance of a sufficient computational infrastructure is costly. Cloud computing allows distributed computing over a network and maximizes the efficiency of the shared resources. Nevertheless, security concerns rise when valuable research data is transferred to a public Cloud. Herein, we describe—from the application and security perspective—three biomedical case studies from different domains: Patent annotation, cancer outcome prediction, and drug target prediction. We elaborate on different strategies to secure the data and results in the Cloud as well as on the infrastructure needed. The proposed Cloud solutions can help to adapt similar algorithms from different domains to benefit from Cloud computing.

Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein ^ protein interaction networks and ranks genes using the random surfer model of Google’s PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein ^ protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression.

Reflexions autour du signalement : le signalement est un regulateur des dysfonctionnements graves qui mettent en danger le developpement d’un enfant. Le risque du signalement est le malentendu. Avant de rediger un ecrit, il est fondamental de passer par un temps d’evaluation pluridisciplinaire en tenant compte des implications a court et a long terme que peut entrainer cette intervention dans le devenir psychique de l’enfant. Le risque est de creer des traumatismes plus destructeurs que les faits eux-memes.