Three-Year Mortality of Older Hospitalized Patients with Osteosarcopenia: Data from the OsteoSys Study.

Osteosarcopenia, the concurrent presence of sarcopenia and osteopenia/osteoporosis, poses a significant health risk to older adults, yet its impact on clinical outcomes is not fully understood. The aim of this prospective, longitudinal multicentre study was to examine the impact of osteosarcopenia on 3-year mortality and unplanned hospitalizations among 572 older hospitalized patients (mean age 75.1 ± 10.8 years, 78% female). Sarcopenia and low bone mineral density (BMD) were evaluated using Dual Energy X-ray Absorptiometry and the European Working Group on Sarcopenia in Older People (EWGSOP2) and WHO criteria, respectively. Among participants, 76% had low BMD, 9% were sarcopenic, and 8% had osteosarcopenia. Individuals with osteosarcopenia experienced a significantly higher rate of mortality (46%, p < 001) and unplanned hospitalization (86%, p < 001) compared to those without this condition. Moreover, "healthy" subjects-those without sarcopenia or low BMD-showed markedly lower 3-year mortality (9%, p < 001) and less unplanned hospitalization (53%, p < 001). The presence of osteosarcopenia (p = 0.009) increased the 3-year mortality risk by 30% over sarcopenia alone and by 8% over low BMD alone, underscoring the severe health implications of concurrent muscle and bone deterioration. This study highlights the substantial impact of osteosarcopenia on mortality among older adults, emphasizing the need for targeted diagnostic and therapeutic strategies.

Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics.

Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing. Mibianto solves this problem by operating directly on read files, without manual preprocessing or complete data exchange. It handles diverse sequencing platforms, including short single-end, paired-end, and long read technologies. Our sketch-based workflow significantly reduces the data volume transferred from the user to the server (up to 99.59% size reduction) to subsequently perform taxonomic profiling with enhanced efficiency and privacy. Mibianto offers functionality beyond k-mer quantification; it supports advanced community composition estimation, including diversity, ordination, and differential abundance analysis. Our tool aids in the standardization of computational workflows, thus supporting reproducibility of scientific sequencing studies. It is adaptable to small- and large-scale experimental designs and offers a user-friendly interface, thus making it an invaluable tool for both clinical and research-oriented metagenomic studies. Mibianto is freely available without the need for a login at: https://www.ccb.uni-saarland.de/mibianto.

EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.

MOTIVATION: Automated chromatin segmentation based on ChIP-seq (chromatin immunoprecipitation followed by sequencing) data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality. RESULTS: We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with existing tools, ChromHMM, Segway, and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states. AVAILABILITY AND IMPLEMENTATION: Source code: https://gitlab.com/rahmannlab/episegmix.

Pancreatic cancer acquires resistance to MAPK pathway inhibition by clonal expansion and adaptive DNA hypermethylation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis. It is marked by extraordinary resistance to conventional therapies including chemotherapy and radiation, as well as to essentially all targeted therapies evaluated so far. More than 90% of PDAC cases harbor an activating KRAS mutation. As the most common KRAS variants in PDAC remain undruggable so far, it seemed promising to inhibit a downstream target in the MAPK pathway such as MEK1/2, but up to now preclinical and clinical evaluation of MEK inhibitors (MEKi) failed due to inherent and acquired resistance mechanisms. To gain insights into molecular changes during the formation of resistance to oncogenic MAPK pathway inhibition, we utilized short-term passaged primary tumor cells from ten PDACs of genetically engineered mice. We followed gain and loss of resistance upon MEKi exposure and withdrawal by longitudinal integrative analysis of whole genome sequencing, whole genome bisulfite sequencing, RNA-sequencing and mass spectrometry data. RESULTS: We found that resistant cell populations under increasing MEKi treatment evolved by the expansion of a single clone but were not a direct consequence of known resistance-conferring mutations. Rather, resistant cells showed adaptive DNA hypermethylation of 209 and hypomethylation of 8 genomic sites, most of which overlap with regulatory elements known to be active in murine PDAC cells. Both DNA methylation changes and MEKi resistance were transient and reversible upon drug withdrawal. Furthermore, MEKi resistance could be reversed by DNA methyltransferase inhibition with remarkable sensitivity exclusively in the resistant cells. CONCLUSION: Overall, the concept of acquired therapy resistance as a result of the expansion of a single cell clone with epigenetic plasticity sheds light on genetic, epigenetic and phenotypic patterns during evolvement of treatment resistance in a tumor with high adaptive capabilities and provides potential for reversion through epigenetic targeting.

Non-responsiveness to cardioprotection by ischaemic preconditioning in Ossabaw minipigs with genetic predisposition to, but without the phenotype of the metabolic syndrome.

The translation of successful preclinical and clinical proof-of-concept studies on cardioprotection to the benefit of patients with reperfused acute myocardial infarction has been difficult so far. This difficulty has been attributed to confounders which patients with myocardial infarction typically have but experimental animals usually not have. The metabolic syndrome is a typical confounder. We hypothesised that there may also be a genuine non-responsiveness to cardioprotection and used Ossabaw minipigs which have the genetic predisposition to develop a diet-induced metabolic syndrome, but before they had developed the diseased phenotype. Using a prospective study design, a reperfused acute myocardial infarction was induced in 62 lean Ossabaw minipigs by 60 min coronary occlusion and 180 min reperfusion. Ischaemic preconditioning by 3 cycles of 5 min coronary occlusion and 10 min reperfusion was used as cardioprotective intervention. Ossabaw minipigs were stratified for their single nucleotide polymorphism as homozygous for valine (V/V) or isoleucine (I/I)) in the γ-subunit of adenosine monophosphate-activated protein kinase. Endpoints were infarct size and area of no-reflow. Infarct size (V/V: 54 ± 8, I/I: 54 ± 13% of area at risk, respectively) was not reduced by ischaemic preconditioning (V/V: 55 ± 11, I/I: 46 ± 11%) nor was the area of no-reflow (V/V: 57 ± 18, I/I: 49 ± 21 vs. V/V: 57 ± 21, I/I: 47 ± 21% of infarct size). Bioinformatic comparison of the Ossabaw genome to that of Sus scrofa and Göttingen minipigs identified differences in clusters of genes encoding mitochondrial and inflammatory proteins, including the janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. The phosphorylation of STAT3 at early reperfusion was not increased by ischaemic preconditioning, different from the established STAT3 activation by cardioprotective interventions in other pig strains. Ossabaw pigs have not only the genetic predisposition to develop a metabolic syndrome but also are not amenable to cardioprotection by ischaemic preconditioning.

Detection and Validation of Circular DNA Fragments Using Nanopore Sequencing.

Occurrence of extra-chromosomal circular DNA is a phenomenon frequently observed in tumor cells, and the presence of such DNA has been recognized as a marker of adverse outcome across cancer types. We here describe a computational workflow for identification of DNA circles from long-read sequencing data. The workflow is implemented based on the Snakemake workflow management system. Its key step uses a graph-theoretic approach to identify putative circular fragments validated on simulated reads. We then demonstrate robustness of our approach using nanopore sequencing of selectively enriched circular DNA by highly sensitive and specific recovery of plasmids and the mitochondrial genome, which is the only circular DNA in normal human cells. Finally, we show that the workflow facilitates detection of larger circular DNA fragments containing extrachromosomal copies of the MYCN oncogene and the respective breakpoints, which is a potentially useful application in disease monitoring of several cancer types.

Osteosarcopenia, an Asymmetrical Overlap of Two Connected Syndromes: Data from the OsteoSys Study.

Osteoporosis and sarcopenia are two chronic conditions, which widely affect older people and share common risk factors. We investigated the prevalence of low bone mineral density (BMD) and sarcopenia, including the overlap of both conditions (osteosarcopenia) in 572 older hospitalized patients (mean age 75.1 ± 10.8 years, 78% women) with known or suspected osteoporosis in this prospective observational multicenter study. Sarcopenia was assessed according to the revised definition of the European Working Group on Sarcopenia in Older People (EWGSOP2). Low BMD was defined according to the World Health Organization (WHO) recommendations as a T-score < -1.0. Osteosarcopenia was diagnosed when both low BMD and sarcopenia were present. Low BMD was prevalent in 76% and the prevalence of sarcopenia was 9%, with 90% of the sarcopenic patients showing the overlap of osteosarcopenia (8% of the entire population). Conversely, only few patients with low BMD demonstrated sarcopenia (11%). Osteosarcopenic patients were older and frailer and had lower BMI, fat, and muscle mass, handgrip strength, and T-score compared to nonosteosarcopenic patients. We conclude that osteosarcopenia is extremely common in sarcopenic subjects. Considering the increased risk of falls in patients with sarcopenia, they should always be evaluated for osteoporosis.

Sustainable data analysis with Snakemake.

Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.

Rapid T-cell receptor interaction grouping with ting.

MOTIVATION: Clustering T-cell receptor repertoire (TCRR) sequences according to antigen specificity is challenging. The previously published tool GLIPH needs several days to weeks for clustering large repertoires, making its use impractical in larger studies. In addition, the methodology used in GLIPH suffers from shortcomings, including non-determinism, potential loss of significant antigen-specific sequences or inclusion of too many unspecific sequences. RESULTS: We present an algorithm for clustering TCRR sequences that scales efficiently to large repertoires. We clustered 36 real datasets with up to 62 000 unique CDR3ß sequences using both an implementation of our method called ting, GLIPH and its successor GLIPH2. While GLIPH required multiple weeks, ting only needed about one minute for the same task. GLIPH2 is comparably fast, but uses a different grouping paradigm. In addition, we found that in naïve repertoires, where no or very few antigen-specific CDR3 sequences or clusters should exist, our method indeed selects much fewer motifs and produces smaller clusters. AVAILABILITY AND IMPLEMENTATION: Our method has been implemented in Python as a tool called ting. It is available from GitHub (https://github.com/FelixMoelder/ting) or PyPI under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fast lightweight accurate xenograft sorting.

MOTIVATION: With an increasing number of patient-derived xenograft (PDX) models being created and subsequently sequenced to study tumor heterogeneity and to guide therapy decisions, there is a similarly increasing need for methods to separate reads originating from the graft (human) tumor and reads originating from the host species' (mouse) surrounding tissue. Two kinds of methods are in use: On the one hand, alignment-based tools require that reads are mapped and aligned (by an external mapper/aligner) to the host and graft genomes separately first; the tool itself then processes the resulting alignments and quality metrics (typically BAM files) to assign each read or read pair. On the other hand, alignment-free tools work directly on the raw read data (typically FASTQ files). Recent studies compare different approaches and tools, with varying results. RESULTS: We show that alignment-free methods for xenograft sorting are superior concerning CPU time usage and equivalent in accuracy. We improve upon the state of the art sorting by presenting a fast lightweight approach based on three-way bucketed quotiented Cuckoo hashing. Our hash table requires memory comparable to an FM index typically used for read alignment and less than other alignment-free approaches. It allows extremely fast lookups and uses less CPU time than other alignment-free methods and alignment-based methods at similar accuracy. Several engineering steps (e.g., shortcuts for unsuccessful lookups, software prefetching) improve the performance even further. AVAILABILITY: Our software xengsort is available under the MIT license at http://gitlab.com/genomeinformatics/xengsort . It is written in numba-compiled Python and comes with sample Snakemake workflows for hash table construction and dataset processing.

GAMIBHEAR: whole-genome haplotype reconstruction from Genome Architecture Mapping data.

MOTIVATION: Genome Architecture Mapping (GAM) was recently introduced as a digestion- and ligation-free method to detect chromatin conformation. Orthogonal to existing approaches based on chromatin conformation capture (3C), GAM's ability to capture both inter- and intra-chromosomal contacts from low amounts of input data makes it particularly well suited for allele-specific analyses in a clinical setting. Allele-specific analyses are powerful tools to investigate the effects of genetic variants on many cellular phenotypes including chromatin conformation, but require the haplotypes of the individuals under study to be known a priori. So far, however, no algorithm exists for haplotype reconstruction and phasing of genetic variants from GAM data, hindering the allele-specific analysis of chromatin contact points in non-model organisms or individuals with unknown haplotypes. RESULTS: We present GAMIBHEAR, a tool for accurate haplotype reconstruction from GAM data. GAMIBHEAR aggregates allelic co-observation frequencies from GAM data and employs a GAM-specific probabilistic model of haplotype capture to optimize phasing accuracy. Using a hybrid mouse embryonic stem cell line with known haplotype structure as a benchmark dataset, we assess correctness and completeness of the reconstructed haplotypes, and demonstrate the power of GAMIBHEAR to infer accurate genome-wide haplotypes from GAM data. AVAILABILITY AND IMPLEMENTATION: GAMIBHEAR is available as an R package under the open-source GPL-2 license at https://bitbucket.org/schwarzlab/gamibhear. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteomic and bioinformatic profiling of neutrophils in CLL reveals functional defects that predispose to bacterial infections.

Patients with chronic lymphocytic leukemia (CLL) typically suffer from frequent and severe bacterial infections. Although it is well known that neutrophils are critical innate immune cells facilitating the early defense, the underlying phenotypical and functional changes in neutrophils during CLL remain largely elusive. Using a murine adoptive transfer model of CLL, we demonstrate aggravated bacterial burden in CLL-bearing mice upon a urinary tract infection with uropathogenic Escherichia coli. Bioinformatic analyses of the neutrophil proteome revealed increased expression of proteins associated with interferon signaling and decreased protein expression associated with granule composition and neutrophil migration. Functional experiments validated these findings by showing reduced levels of myeloperoxidase and acidification of neutrophil granules after ex vivo phagocytosis of bacteria. Pathway enrichment analysis indicated decreased expression of molecules critical for neutrophil recruitment, and migration of neutrophils into the infected urinary bladder was significantly reduced. These altered migratory properties of neutrophils were also associated with reduced expression of CD62L and CXCR4 and correlated with an increased incidence of infections in patients with CLL. In conclusion, this study describes a molecular signature of neutrophils through proteomic, bioinformatic, and functional analyses that are linked to a reduced migratory ability, potentially leading to increased bacterial infections in patients with CLL.

Detecting high-scoring local alignments in pangenome graphs.

MOTIVATION: Increasing amounts of individual genomes sequenced per species motivate the usage of pangenomic approaches. Pangenomes may be represented as graphical structures, e.g. compacted colored de Bruijn graphs, which offer a low memory usage and facilitate reference-free sequence comparisons. While sequence-to-graph mapping to graphical pangenomes has been studied for some time, no local alignment search tool in the vein of BLAST has been proposed yet. RESULTS: We present a new heuristic method to find maximum scoring local alignments of a DNA query sequence to a pangenome represented as a compacted colored de Bruijn graph. Our approach additionally allows a comparison of similarity among sequences within the pangenome. We show that local alignment scores follow an exponential-tail distribution similar to BLAST scores, and we discuss how to estimate its parameters to separate local alignments representing sequence homology from spurious findings. An implementation of our method is presented, and its performance and usability are shown. Our approach scales sublinearly in running time and memory usage with respect to the number of genomes under consideration. This is an advantage over classical methods that do not make use of sequence similarity within the pangenome. AVAILABILITY AND IMPLEMENTATION: Source code and test data are available from https://gitlab.ub.uni-bielefeld.de/gi/plast. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Epitope similarity cannot explain the pre-formed T cell immunity towards structural SARS-CoV-2 proteins.

The current pandemic is caused by the SARS-CoV-2 virus and large progress in understanding the pathology of the virus has been made since its emergence in late 2019. Several reports indicate short lasting immunity against endemic coronaviruses, which contrasts studies showing that biobanked venous blood contains T cells reactive to SARS-CoV-2 S-protein even before the outbreak in Wuhan. This suggests a preformed T cell memory towards structural proteins in individuals not exposed to SARS-CoV-2. Given the similarity of SARS-CoV-2 to other members of the Coronaviridae family, the endemic coronaviruses appear likely candidates to generate this T cell memory. However, given the apparent poor immunological memory created by the endemic coronaviruses, immunity against other common pathogens might offer an alternative explanation. Here, we utilize a combination of epitope prediction and similarity to common human pathogens to identify potential sources of the SARS-CoV-2 T cell memory. Although beta-coronaviruses are the most likely candidates to explain the pre-existing SARS-CoV-2 reactive T cells in uninfected individuals, the SARS-CoV-2 epitopes with the highest similarity to those from beta-coronaviruses are confined to replication associated proteins-not the host interacting S-protein. Thus, our study suggests that the observed SARS-CoV-2 pre-formed immunity to structural proteins is not driven by near-identical epitopes.

Machine learning reveals a PD-L1-independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context.

OBJECTIVE: Current predictive biomarkers for PD-1 (programmed cell death protein 1)/PD-L1 (programmed death-ligand 1)-directed immunotherapy in non-small cell lung cancer (NSCLC) mostly focus on features of tumour cells. However, the tumour microenvironment and immune context are expected to play major roles in governing therapy response. Against this background, we set out to apply context-sensitive feature selection and machine learning approaches on expression profiles of immune-related genes in diagnostic biopsies of patients with stage IV NSCLC. METHODS: RNA expression levels were determined using the NanoString nCounter platform in formalin-fixed paraffin-embedded tumour biopsies obtained during the diagnostic workup of stage IV NSCLC from two thoracic oncology centres. A 770-gene panel covering immune-related genes and control genes was used. We applied supervised machine learning methods for feature selection and generation of predictive models. RESULTS: Feature selection and model creation were based on a training cohort of 55 patients with recurrent NSCLC treated with PD-1/PD-L1 antibody therapy. Resulting models identified patients with superior outcomes to immunotherapy, as validated in two subsequently recruited, separate patient cohorts (n = 67, hazard ratio = 0.46, p = 0.035). The predictive information obtained from these models was orthogonal to PD-L1 expression as per immunohistochemistry: Selecting by PD-L1 positivity at immunohistochemistry plus model prediction identified patients with highly favourable outcomes. Independence of PD-L1 positivity and model predictions were confirmed in multivariate analysis. Visualisation of the models revealed the predictive superiority of the entire 7-gene context over any single gene. CONCLUSION: Using context-sensitive assays and bioinformatics capturing the tumour immune context allows precise prediction of response to PD-1/PD-L1-directed immunotherapy in NSCLC.

Meiotic recombination in the offspring of Microbotryum hybrids and its impact on pathogenicity.

BACKGROUND: Hybridization is a central mechanism in evolution, producing new species or introducing important genetic variation into existing species. In plant-pathogenic fungi, adaptation and specialization to exploit a host species are key determinants of evolutionary success. Here, we performed experimental crosses between the two pathogenic Microbotryum species, M. lychnidis-dioicae and M. silenes-acaulis that are specialized to different hosts. The resulting offspring were analyzed on phenotypic and genomic levels to describe genomic characteristics of hybrid offspring and genetic factors likely involved in host-specialization. RESULTS: Genomic analyses of interspecific fungal hybrids revealed that individuals were most viable if the majority of loci were inherited from one species. Interestingly, species-specific loci were strictly controlled by the species' origin of the mating type locus. Moreover we detected signs of crossing over and chromosome duplications in the genomes of the analyzed hybrids. In Microbotryum, mitochondrial DNA was found to be uniparentally inherited from the a2 mating type. Genome comparison revealed that most gene families are shared and the majority of genes are conserved between the two species, indicating very similar biological features, including infection and pathogenicity processes. Moreover, we detected 211 candidate genes that were retained under host-driven selection of backcrossed lines. These genes and might therefore either play a crucial role in host specialization or be linked to genes that are essential for specialization. CONCLUSION: The combination of genome analyses with experimental selection and hybridization is a promising way to investigate host-pathogen interactions. This study manifests genetic factors of host specialization that are required for successful biotrophic infection of the post-zygotic stage, but also demonstrates the strong influence of intra-genomic conflicts or instabilities on the viability of hybrids in the haploid host-independent stage.

A germ cell-specific ageing pattern in otherwise healthy men.

Life-long sperm production leads to the assumption that male fecundity remains unchanged throughout life. However, recently it was shown that paternal age has profound consequences for male fertility and offspring health. Paternal age effects are caused by an accumulation of germ cell mutations over time, causing severe congenital diseases. Apart from these well-described cases, molecular patterns of ageing in germ cells and their impact on DNA integrity have not been studied in detail. In this study, we aimed to assess the effects of 'pure' ageing on male reproductive health and germ cell quality. We assembled a cohort of 198 healthy men (18-84 years) for which end points such as semen and hormone profiles, sexual health and well-being, and sperm DNA parameters were evaluated. Sperm production and hormonal profiles were maintained at physiological levels over a period of six decades. In contrast, we identified a germ cell-specific ageing pattern characterized by a steady increase of telomere length in sperm and a sharp increase in sperm DNA instability, particularly after the sixth decade. Importantly, we found sperm DNA methylation changes in 236 regions, mostly nearby genes associated with neuronal development. By in silico analysis, we found that 10 of these regions are located in loci which can potentially escape the first wave of genome-wide demethylation after fertilization. In conclusion, human male germ cells present a unique germline-specific ageing process, which likely results in diminished fecundity in elderly men and poorer health prognosis for their offspring.

wg-blimp: an end-to-end analysis pipeline for whole genome bisulfite sequencing data.

BACKGROUND: Analysing whole genome bisulfite sequencing datasets is a data-intensive task that requires comprehensive and reproducible workflows to generate valid results. While many algorithms have been developed for tasks such as alignment, comprehensive end-to-end pipelines are still sparse. Furthermore, previous pipelines lack features or show technical deficiencies, thus impeding analyses. RESULTS: We developed wg-blimp (whole genome bisulfite sequencing methylation analysis pipeline) as an end-to-end pipeline to ease whole genome bisulfite sequencing data analysis. It integrates established algorithms for alignment, quality control, methylation calling, detection of differentially methylated regions, and methylome segmentation, requiring only a reference genome and raw sequencing data as input. Comparing wg-blimp to previous end-to-end pipelines reveals similar setups for common sequence processing tasks, but shows differences for post-alignment analyses. We improve on previous pipelines by providing a more comprehensive analysis workflow as well as an interactive user interface. To demonstrate wg-blimp's ability to produce correct results we used it to call differentially methylated regions for two publicly available datasets. We were able to replicate 112 of 114 previously published regions, and found results to be consistent with previous findings. We further applied wg-blimp to a publicly available sample of embryonic stem cells to showcase methylome segmentation. As expected, unmethylated regions were in close proximity of transcription start sites. Segmentation results were consistent with previous analyses, despite different reference genomes and sequencing techniques. CONCLUSIONS: wg-blimp provides a comprehensive analysis pipeline for whole genome bisulfite sequencing data as well as a user interface for simplified result inspection. We demonstrated its applicability by analysing multiple publicly available datasets. Thus, wg-blimp is a relevant alternative to previous analysis pipelines and may facilitate future epigenetic research.

Improved risk-stratification for posterior fossa ependymoma of childhood considering clinical, histological and genetic features - a retrospective analysis of the HIT ependymoma trial cohort.

INTRODUCTION: Risk stratification of children with ependymomas of the posterior fossa in current therapeutic protocols is mainly based on clinical criteria. We aimed to identify independent outcome predictors for this disease entity by a systematic integrated analysis of clinical, histological and genetic information in a defined cohort of patients treated according to the German HIT protocols. METHODS: Tumor samples of 134 patients aged 0.2-15.9 years treated between 1999 and 2010 according to HIT protocols were analyzed for histological features including mitotic activity, necrosis and vascular proliferation and genomic alterations by SNP and molecular inversion probe analysis. Survival analysis was performed by Kaplan-Meier method with log rank test and multivariate Cox regression analysis. RESULTS: Residual tumor after surgery, chromosome 1q gain and structural genomic alterations were identified as predictors of significantly shorter event-free (EFS) and overall survival (OS). Furthermore, specific histological features including vascular proliferation, necrosis and high mitotic activity were predictive for shorter OS. Multivariate Cox regression revealed residual tumor, chromosome 1q gain and mitotic activity as independent predictors of both EFS and OS. Using these independent predictors of outcome, we were able to build a 3-tiered risk stratification model that separates patients with standard, intermediate and high risk, and which outperforms current stratification procedures. CONCLUSION: The integration of defined clinical, histological and genetic parameters led to an improved risk-stratification model for posterior fossa ependymoma of childhood. After validation in independent cohorts this model may provide the basis for risk-adapted treatment of children with ependymomas of the posterior fossa.