Pustular psoriasis: Molecular pathways and effects of spesolimab in generalized pustular psoriasis.

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: We sought to compare the molecular profiles of lesional and nonlesional 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 upregulated or downregulated. Selected transcripts from the IL-36 signaling pathway were upregulated in untreated GPP and PPP lesions. In patients with GPP, IL-36 pathway-related signatures, TH1/TH17 and innate inflammation signaling, 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+, and IL-36γ+ cells and lipocalin-2-expressing cells. CONCLUSIONS: 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.

Peripheral blood proteomic profiling of idiopathic pulmonary fibrosis biomarkers in the multicentre IPF-PRO Registry.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease for which diagnosis and management remain challenging. Defining the circulating proteome in IPF may identify targets for biomarker development. We sought to quantify the circulating proteome in IPF, determine differential protein expression between subjects with IPF and controls, and examine relationships between protein expression and markers of disease severity. METHODS: This study involved 300 patients with IPF from the IPF-PRO Registry and 100 participants without known lung disease. Plasma collected at enrolment was analysed using aptamer-based proteomics (1305 proteins). Linear regression was used to determine differential protein expression between participants with IPF and controls and associations between protein expression and disease severity measures (percent predicted values for forced vital capacity [FVC] and diffusion capacity of the lung for carbon monoxide [DLco]; composite physiologic index [CPI]). Multivariable models were fit to select proteins that best distinguished IPF from controls. RESULTS: Five hundred fifty one proteins had significantly different levels between IPF and controls, of which 47 showed a |log2(fold-change)| > 0.585 (i.e. > 1.5-fold difference). Among the proteins with the greatest difference in levels in patients with IPF versus controls were the glycoproteins thrombospondin 1 and von Willebrand factor and immune-related proteins C-C motif chemokine ligand 17 and bactericidal permeability-increasing protein. Multivariable classification modelling identified nine proteins that, when considered together, distinguished IPF versus control status with high accuracy (area under receiver operating curve = 0.99). Among participants with IPF, 14 proteins were significantly associated with FVC % predicted, 23 with DLco % predicted, 14 with CPI. Four proteins (roundabout homolog-2, spondin-1, polymeric immunoglobulin receptor, intercellular adhesion molecule 5) demonstrated the expected relationship across all three disease severity measures. When considered in pathways analyses, proteins associated with the presence or severity of IPF were enriched in pathways involved in platelet and haemostatic responses, vascular or platelet derived growth factor signalling, immune activation, and extracellular matrix organisation. CONCLUSIONS: Patients with IPF have a distinct circulating proteome and can be distinguished using a nine-protein profile. Several proteins strongly associate with disease severity. The proteins identified may represent biomarker candidates and implicate pathways for further investigation. TRIAL REGISTRATION: ClinicalTrials.gov (NCT01915511).

Silenced ZNF154 Is Associated with Longer Survival in Resectable Pancreatic Cancer.

Pancreatic cancer has become the third leading cause of cancer-related death in the Western world despite advances in therapy of other cancerous lesions. Late diagnosis due to a lack of symptoms during early disease allows metastatic spread of the tumor. Most patients are considered incurable because of metastasized disease. On a cellular level, pancreatic cancer proves to be rather resistant to chemotherapy. Hence, early detection and new therapeutic targets might improve outcomes. The detection of DNA promoter hypermethylation has been described as a method to identify putative genes of interest in cancer entities. These genes might serve as either biomarkers or might lead to a better understanding of the molecular mechanisms involved. We checked tumor specimens from 80 patients who had undergone pancreatic resection for promoter hypermethylation of the zinc finger protein ZNF154. Then, we further characterized the effects of ZNF154 on cell viability and gene expression by in vitro experiments. We found a significant association between ZNF154 hypermethylation and better survival in patients with resectable pancreatic cancer. Moreover, we suspect that the cell growth suppressor SLFN5 might be linked to a silenced ZNF154 in pancreatic cancer.

Network information improves cancer outcome prediction.

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.

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

Interleukin 36 receptor-inducible matrix metalloproteinase 13 mediates intestinal fibrosis.

Background: Fibrostenotic disease is a common complication in Crohn's disease (CD) patients hallmarked by transmural extracellular matrix (ECM) accumulation in the intestinal wall. The prevention and medical therapy of fibrostenotic CD is an unmet high clinical need. Although targeting IL36R signaling is a promising therapy option, downstream mediators of IL36 during inflammation and fibrosis have been incompletely understood. Candidate molecules include matrix metalloproteinases which mediate ECM turnover and are thereby potential targets for anti-fibrotic treatment. Here, we have focused on understanding the role of MMP13 during intestinal fibrosis. Methods: We performed bulk RNA sequencing of paired colon biopsies taken from non-stenotic and stenotic areas of patients with CD. Corresponding tissue samples from healthy controls and CD patients with stenosis were used for immunofluorescent (IF) staining. MMP13 gene expression was analyzed in cDNA of intestinal biopsies from healthy controls and in subpopulations of patients with CD in the IBDome cohort. In addition, gene regulation on RNA and protein level was studied in colon tissue and primary intestinal fibroblasts from mice upon IL36R activation or blockade. Finally, in vivo studies were performed with MMP13 deficient mice and littermate controls in an experimental model of intestinal fibrosis. Ex vivo tissue analysis included Masson's Trichrome and Sirius Red staining as well as evaluation of immune cells, fibroblasts and collagen VI by IF analysis. Results: Bulk RNA sequencing revealed high upregulation of MMP13 in colon biopsies from stenotic areas, as compared to non-stenotic regions of patients with CD. IF analysis confirmed higher levels of MMP13 in stenotic tissue sections of CD patients and demonstrated αSMA+ and Pdpn+ fibroblasts as a major source. Mechanistic experiments demonstrated that MMP13 expression was regulated by IL36R signaling. Finally, MMP13 deficient mice, as compared to littermate controls, developed less fibrosis in the chronic DSS model and showed reduced numbers of αSMA+ fibroblasts. These findings are consistent with a model suggesting a molecular axis involving IL36R activation in gut resident fibroblasts and MMP13 expression during the pathogenesis of intestinal fibrosis. Conclusion: Targeting IL36R-inducible MMP13 could evolve as a promising approach to interfere with the development and progression of intestinal fibrosis.

Differential Effects of Trp53 Alterations in Murine Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) development is a multi-step process resulting in the accumulation of genetic alterations. Despite its high incidence, there are currently no mouse models that accurately recapitulate this process and mimic sporadic CRC. We aimed to develop and characterize a genetically engineered mouse model (GEMM) of Apc/Kras/Trp53 mutant CRC, the most frequent genetic subtype of CRC. METHODS: Tumors were induced in mice with conditional mutations or knockouts in Apc, Kras, and Trp53 by a segmental adeno-cre viral infection, monitored via colonoscopy and characterized on multiple levels via immunohistochemistry and next-generation sequencing. RESULTS: The model accurately recapitulates human colorectal carcinogenesis clinically, histologically and genetically. The Trp53 R172H hotspot mutation leads to significantly increased metastatic capacity. The effects of Trp53 alterations, as well as the response to treatment of this model, are similar to human CRC. Exome sequencing revealed spontaneous protein-modifying alterations in multiple CRC-related genes and oncogenic pathways, resulting in a genetic landscape resembling human CRC. CONCLUSIONS: This model realistically mimics human CRC in many aspects, allows new insights into the role of TP53 in CRC, enables highly predictive preclinical studies and demonstrates the value of GEMMs in current translational cancer research and drug development.

Meta-analysis of Cancer Gene Profiling Data.

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.

Gene Expression Analysis in the Age of Mass Sequencing: An Introduction.

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.