Explore the vast range of titles available.

Background Mutational signatures are specific patterns of somatic mutations introduced into the genome by oncogenic processes. Several mutational signatures have been identified and quantified from multiple cancer studies, and some of them have been linked to known oncogenic processes. Identification of the processes contributing to mutations observed in a sample is potentially informative to understand the cancer etiology. Results We present here SigsPack , a Bioconductor package to estimate a sample’s exposure to mutational processes described by a set of mutational signatures. The package also provides functions to estimate stability of these exposures, using bootstrapping. The performance of exposure and exposure stability estimations have been validated using synthetic and real data. Finally, the package provides tools to normalize the mutation frequencies with respect to the tri-nucleotide contents of the regions probed in the experiment. The importance of this effect is illustrated in an example. Conclusion SigsPack provides a complete set of tools for individual sample exposure estimation, and for mutation catalogue & mutational signatures normalization.

Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and via the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospho-proteomic profiling confirmed the cell-specific feedback effects and synergy of MEK and IGFR targeted treatment. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies. Our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma.

Single cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication. To facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, define cell groups by filtering or manual selection and perform differential gene expression, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. We test and validate our visualization using publicly available scRNA-seq data. SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis.

Background Immune escape is one of the hallmarks of cancer and several new treatment approaches attempt to modulate and restore the immune system’s capability to target cancer cells. At the heart of the immune recognition process lies antigen presentation from somatic mutations. These neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority. Methods We carefully screen TCGA data sets for recurrent somatic amino acid exchanges and apply MHC class I binding predictions. Results We propose a method for in silico selection and prioritization of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. While the percentage of patients carrying a specific neo-epitope and HLA-type combination is relatively small, the sheer number of new patients leads to surprisingly high reoccurence numbers. We identify 769 epitopes which are expected to occur in 77629 patients per year. Conclusion While our candidate list will definitely contain false positives, the results provide an objective order for wet-lab testing of reusable neo-epitopes. Thus recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.

Background Enhancer of zeste homolog 2 (EZH2) is considered an important driver of tumor development and progression by its histone modifying capabilities. Inhibition of EZH2 activity is thought to be a potent treatment option for eligible cancer patients with an aberrant EZH2 expression profile, thus the indirect EZH2 inhibitor 3-Deazaneplanocin A (DZNep) is currently under evaluation for its clinical utility. Although DZNep blocks proliferation and induces apoptosis in different tumor types including lymphomas, acquired resistance to DZNep may limit its clinical application. Methods To investigate possible mechanisms of acquired DZNep resistance in B-cell lymphomas, we generated a DZNep-resistant clone from a previously DZNep-sensitive B-cell lymphoma cell line by long-term treatment with increasing concentrations of DZNep (ranging from 200 to 2000 nM) and compared the molecular profiles of resistant and wild-type clones. This comparison was done using molecular techniques such as flow cytometry, copy number variation assay (OncoScan and TaqMan assays), fluorescence in situ hybridization, Western blot, immunohistochemistry and metabolomics analysis. Results Whole exome sequencing did not indicate the acquisition of biologically meaningful single nucleotide variants. Analysis of copy number alterations, however, demonstrated among other acquired imbalances an amplification (about 30 times) of the S-adenosyl-L-homocysteine hydrolase ( AHCY ) gene in the resistant clone. AHCY is a direct target of DZNep and is critically involved in the biological methylation process, where it catalyzes the reversible hydrolysis of S-adenosyl-L-homocysteine to L-homocysteine and adenosine. The amplification of the AHCY gene is paralleled by strong overexpression of AHCY at both the transcriptional and protein level, and persists upon culturing the resistant clone in a DZNep-free medium. Conclusions This study reveals one possible molecular mechanism how B-cell lymphomas can acquire resistance to DZNep, and proposes AHCY as a potential biomarker for investigation during the administration of EZH2-targeted therapy with DZNep.

Recently, several genome-wide association studies identified PHACTR1 as key locus for five diverse vascular disorders: coronary artery disease, migraine, fibromuscular dysplasia, cervical artery dissection and hypertension. Although these represent significant risk factors or comorbidities for ischemic stroke, PHACTR1 role in brain small vessel ischemic disease and ischemic stroke most important survival mechanism, such as the recruitment of brain collateral arteries like posterior communicating arteries (PcomAs), remains unknown. Therefore, we applied exome and genome sequencing in a multi-ethnic cohort of 180 early-onset independent familial and apparently sporadic brain small vessel ischemic disease and CADASIL-like Caucasian patients from US, Portugal, Finland, Serbia and Turkey and in 2 C57BL/6J stroke mouse models (bilateral common carotid artery stenosis [BCCAS] and middle cerebral artery occlusion [MCAO]), characterized by different degrees of PcomAs patency. We report 3 very rare coding variants in the small vessel ischemic disease-CADASIL-like cohort (p.Glu198Gln, p.Arg204Gly, p.Val251Leu) and a stop-gain mutation (p.Gln273*) in one MCAO mouse. These coding variants do not cluster in PHACTR1 known pathogenic domains and are not likely to play a critical role in small vessel ischemic disease or brain collateral circulation. We also exclude the possibility that copy number variants (CNVs) or a variant enrichment in Phactr1 may be associated with PcomA recruitment in BCCAS mice or linked to diverse vascular traits (cerebral blood flow pre-surgery, PcomA size, leptomeningeal microcollateral length and junction density during brain hypoperfusion) in C57BL/6J mice, respectively. Genetic variability in PHACTR1 is not likely to be a common susceptibility factor influencing small vessel ischemic disease in patients and PcomA recruitment in C57BL/6J mice. Nonetheless, rare variants in PHACTR1 RPEL domains may influence the stroke outcome and are worth investigating in a larger cohort of small vessel ischemic disease patients, different ischemic stroke subtypes and with functional studies.

Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and via the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospho-proteomic profiling confirmed the cell-specific feedback effects and synergy of MEK and IGFR targeted treatment. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies. Our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma. Author summary Only few targeted therapies are currently available to treat high-risk neuroblastoma. To address this issue we characterized the drug response of high risk neuroblastoma cell lines and correlated it with genomic and transcriptomic data. Particularly for MEK inhibition, we saw that our panel could be nicely separated into two groups of resistant and sensitive cell lines. Genomic and transcriptomic markers alone did not help to discriminate between responders and non-responders. We used signalling perturbation data to build cell line specific signalling models. Our models suggest that negative feedbacks within MAPK signalling lead to a stronger reactivation of MEK in MEKi resistant cell lines after MEK inhibition. Model analysis suggested that combining MEK inhibition with IGF1R or RAF inhibition could be an effective treatment and we characterised this combination using phosphoproteomics by mass-spectrometry and growth assays. Our study confirms the importance of quantitative understanding of signalling and may help plan future clinical trials involving MEK inhibition for the treatment of neuroblastoma.

Management raw sequencing data and its preprocessing (conversion into sequences and demultiplexing) remains a challenging topic for groups running sequencing devices. They face many challenges in such efforts and solutions ranging from manual management of spreadsheets to very complex and customized LIMS systems handling much more than just sequencing raw data. In this manuscript, we describe the software package DigestiFlow that focuses on the management of Illumina flow cell sample sheets and raw data. It allows for automated extraction of information from flow cell data and management of sample sheets. Furthermore, it allows for the automated and reproducible conversion of Illumina base calls to sequences and the demultiplexing thereof using bcl2fastq and Picard Tools, followed by quality control report generation.

Managing raw sequencing data and conversion into sequences (demultiplexing) remains a challenging topic for groups running sequencing devices. They face many challenges in such efforts and solutions range from manual management of spreadsheets to very complex and customized LIMS systems handling much more than just sequencing raw data. In this manuscript, we describe the software package DigestiFlow that focuses on the management of Illumina flow cell sample sheets and raw data. Namely, it allows for automated extraction of flow cell raw data information, management of sample sheets, and the automated (and thus reproducible) demultiplexing of Illumina base calls data. Availability and Implementation. The software is available under the MIT license at https://github.com/bihealth/digestiflow-server. The client and demux software components are available via Bioconda.

An ever-increasing number of NGS library preparation protocols used in biomedical research requires complex barcoding schemes. In combination with the economic urge to use deep multiplexing on high volume sequencing devices this has turned the once mundane task of demultiplexing into a complex and error prone analysis step. We present an easy to implement, efficient, flexible, and extendable open source solution to address this challenge. All software is available under the permissive MIT open source license.