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PDF file - 23K, Table S2 contains primer sequences for MassARRAY-based DNA methylation analyses

PDF file - 25K, Table S1 describes patient characteristics

PDF file - 37K, Table S5 contains microRNA expression data obtained by the Illumina BeadChip system

PDF file - 26K, Table S6 contains qPCR-based expression data of candidate miRNAs in different healthy tissues

PDF file - 42K, Table S4 contains the 128 recurrently methylated miRNA promoter regions

Dysregulated microRNA (miRNA) expression contributes to the pathogenesis of hematopoietic malignancies, including chronic lymphocytic leukemia (CLL). However, an understanding of the mechanisms that cause aberrant miRNA transcriptional control is lacking. In this study, we comprehensively investigated the role and extent of miRNA epigenetic regulation in CLL. Genome-wide profiling conducted on 24 CLL and 10 healthy B cell samples revealed global DNA methylation patterns upstream of miRNA sequences that distinguished malignant from healthy cells and identified putative miRNA promoters. Integration of DNA methylation and miRNA promoter data led to the identification of 128 recurrent miRNA targets for aberrant promoter DNA methylation. DNA hypomethylation accounted for more than 60% of all aberrant promoter-associated DNA methylation in CLL, and promoter DNA hypomethylation was restricted to well-defined regions. Individual hyper- and hypomethylated promoters allowed discrimination of CLL samples from healthy controls. Promoter DNA methylation patterns were confirmed in an independent patient cohort, with 11 miRNAs consistently showing an inverse correlation between DNA methylation status and expression level. Together, our findings characterize the role of epigenetic changes in the regulation of miRNA transcription and create a repository of disease-specific promoter regions that may provide additional insights into the pathogenesis of CLL. Cancer Res; 72(15); 3775–85. ©2012 AACR.

To describe hand use development in children with unilateral cerebral palsy who did/did not participate in constraint-induced movement therapy (CIMT) before 7 years of age. The study included 334 participants (18 months–12 years) who were assessed with 1,565 Assisting Hand Assessments (AHAs) and categorized into no intensive training (NIT), CIMT (18 months–7 years), and Baby-CIMT (p = .028), but higher stable limit (p = .076). The age when 90% of development was reached was highest in the CIMT group (p = .014). Although non-significant, the Baby-CIMT group had higher mean curve than NIT and CIMT combined (AHA-18 p = .459, limit p = .477). The CIMT group improved more over time than the NIT group. Intensive training extended the window of development, and Baby-CIMT might promote early development.

Motivation There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data and generate data files that can be analyzed by synergy-finding bioinformatics applications. Results screenwerk is an open-source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control and analyze, report and visualize the results of drug sensitivity screens. Availability and implementation The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy. Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no Supplementary information Supplementary data are available at Bioinformatics online.

The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.

There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data, and generate data files that can be analyzed by synergy-finding bioinformatics applications. metascreen is an open source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. metascreen is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. metascreen is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control, and analyze, report and visualize the results of drug sensitivity screens.Availability and implementationThe R-package and technical documentation is available at https://github.com/Enserink-lab; the R source code is publicly available at https://github.com/Enserink-lab/metascreen under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy/Selected modules will be available through Galaxy, an open-source platform for FAIR data analysis, Norway: https://usegalaxy.no

In this paper we propose PIICM, a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a Permutation-Invariant version of the Intrinsic Co-regionalization Model for multi-output Gaussian Process regression, to predict dose-response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed. We show that the model can accurately predict dose-response in held-out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs.

Ciliopathies are clinically and genetically heterogeneous diseases. We studied three patients from two independent families presenting with features of Joubert syndrome: abnormal breathing pattern during infancy, developmental delay/intellectual disability, cerebellar ataxia, molar tooth sign on magnetic resonance imaging scans, and polydactyly. We identified biallelic loss‐of‐function (LOF) variants in CBY1, segregating with the clinical features of Joubert syndrome in the families. CBY1 localizes to the distal end of the mother centriole, contributing to the formation and function of cilia. In accordance with the clinical and mutational findings in the affected individuals, we demonstrated that depletion of Cby1 in zebrafish causes ciliopathy‐related phenotypes. Levels of CBY1 transcript were found reduced in the patients compared with controls, suggesting degradation of the mutated transcript through nonsense‐mediated messenger RNA decay. Accordingly, we could detect CBY1 protein in fibroblasts from controls, but not from patients by immunofluorescence. Furthermore, we observed reduced ability to ciliate, increased ciliary length, and reduced levels of the ciliary proteins AHI1 and ARL13B in patient fibroblasts. Our data show that CBY1 LOF‐variants cause a ciliopathy with features of Joubert syndrome.

Background Vancomycin trough levels are frequently subtherapeutic in intensive care unit (ICU) patients. The aim of this study was to identify patients at risk of therapeutic failure defined as vancomycin area-under-the-curve0-24 /minimum inhibitory concentration (AUC0-24 /MIC) Methods A prospective observational study of ICU patients ≥ 18 years at initiation of vancomycin therapy was conducted from May 2013 to October 2015. The patients were divided into four groups according to renal function and CRRT-mode as follows: normal- or augmented renal clearance and continuous venovenous hemodialysis or -hemofiltration. Vancomycin peak and trough levels were measured at 24, 48, and 72 hours after therapy initiation. Relevance of vancomycin therapy was retrospectively evaluated based on microbiological results. Results Eighty-three patients were included, median age 54.5 years, 74.5% male, SAPS II score 46, and 90 day mortality 28%. Vancomycin therapy was initiated on ICU-day 8 (IQR, 5-12), with a median treatment time of 7.5 (IQR, 5-12) days. AUC0-24 /MIC > 400 was reached in 81% and 8% with MIC = 1 and 2 mg/L respectively. The CRRT groups had higher AUC0-24 /MIC-ratios than the non-CRRT groups (P Conclusion A MIC-value >1 mg/L and augmented renal clearance, were factors increasing the risk of therapeutic failure. Vancomycin treatments could have been omitted or shortened in most of these patients.

Current studies addressing the influence of interleukin-33 or its receptor (IL-33R/ST2) on development of atopic dermatitis-like inflammation in mice have reported conflicting results. We compared the response in single- and double-deficient IL-33−/−/ST2−/− C57BL/6J BomTac mice in the well-established calcipotriol-induced model of atopic dermatitis. All genotypes (groups of up to 14 mice) developed atopic dermatitis-like inflammation yet we observed no biologically relevant difference between groups in gross anatomy or ear thickness. Moreover, histological examination of skin revealed no differences in mononuclear leukocyte and granulocyte infiltration nor Th2 cytokine levels (IL-4 and IL-13). Finally, skin CD45+ cells and CD3+ cells were found at similar densities across all groups. Our findings indicate that lack of interleukin-33 and its receptor ST2 does not prevent the development of AD-like skin inflammation.

Background Foot disorders affect up to one quarter of the adult population. Plantar fasciopathy is a common cause of foot pain associated with decreased activity level and quality of life. Patient-reported outcome measures are important in assessing the burden of a condition as well as in research on the effects of interventions. The Foot Function Index revised short form (FFI-RS) is a region specific questionnaire frequently used in research. This study aimed to cross-culturally adapt the FFI-RS into Norwegian and to test its psychometric properties. Methods The FFI-RS was translated into Norwegian (FFI-RSN) following international guidelines. 139 patients with foot disorders (88% with plantar fasciopathy) were included at baseline to measure internal consistency, explorative factor analysis, construct validity and floor and ceiling effects. 54 patients were included after 1 week for test-retest reliability and smallest detectable change analyses. 100 patients were included for responsiveness and minimal important change at 3 months. Results Cronbach’s alpha for internal consistency was 0.97 and factor analysis supported the use of the total score of the FFI-RSN. Two out of three predefined hypotheses were confirmed by assessing the construct validity with Spearman’s correlation coefficient. Quadratic weighted Kappa for test-retest reliability showed 0.91 (95% CI 0.86–0.96) and the smallest detectable change was 6.5%. The minimal important change was 8.4% and the area under the receiver operating characteristic curve for responsiveness was 0.78 (95% CI 0.69–0.87). We found no floor or ceiling effects on the total score of the FFI-RSN. Conclusions The present study showed excellent reliability of the FFI-RSN and supports the use of the total score of the questionnaire. Furthermore, we found the FFI-RSN to have acceptable responsiveness in relation to change in general health. Smallest detectable change, minimal important change and responsiveness were presented as novel results of the total score of the FFI-RS. FFI-RSN can be used to evaluate global foot health in clinical or research settings with Norwegian patients suffering from plantar fasciopathy. Trial registration Clinical Trials.gov NCT04207164. Initial release 01.11.19.

Background Chronic brain pathology and pre-stroke cognitive impairment (PCI) is predictive of post-stroke dementia. The aim of the current study was to measure pre-stroke neurodegenerative and vascular disease burden found on brain MRI and to assess the association between pre-stroke imaging pathology and PCI, whilst also looking for potential sex differences. Methods This prospective brain MRI cohort is part of the multicentre Norwegian cognitive impairment after stroke (Nor-COAST) study. Patients hospitalized with acute ischemic or hemorrhagic stroke were included from five participating stroke units. Visual rating scales were used to categorize baseline MRIs (N = 410) as vascular, neurodegenerative, mixed, or normal, based on the presence of pathological imaging findings. Pre-stroke cognition was assessed by interviews of patients or caregivers using the Global Deterioration Scale (GDS). Stroke severity was assessed with the National Institute of Health Stroke Scale (NIHSS). Univariate and multiple logistic regression analyses were performed to investigate the association between imaging markers, PCI, and sex. Results Patients’ (N = 410) mean (SD) age was 73.6 (±11) years; 182 (44%) participants were female, the mean (SD) NIHSS at admittance was 4.1 (±5). In 68% of the participants, at least one pathological imaging marker was found. Medial temporal lobe atrophy (MTA) was present in 30% of patients, white matter hyperintensities (WMH) in 38% of patients and lacunes in 35% of patients. PCI was found in 30% of the patients. PCI was associated with cerebrovascular pathology (OR 2.5; CI = 1.4 to 4.5, p = 0.001) and mixed pathology (OR 3.4; CI = 1.9 to 6.1, p = 0.001) but was not associated with neurodegeneration (OR 1.0; CI = 0.5 to 2.2; p = 0.973). Pathological MRI markers, including MTA and lacunes, were more prevalent among men, as was a history of clinical stroke prior to the index stroke. The OR of PCI for women was not significantly increased (OR 1.2; CI = 0.8 to 1.9; p = 0.3). Conclusions Pre-stroke chronic brain pathology is common in stroke patients, with a higher prevalence in men. Vascular pathology and mixed pathology are associated with PCI. There were no significant sex differences for the risk of PCI. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

High-throughput sequencing is increasingly favoured to assay the presence and abundance of micro RNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. However, although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling

AIM To describe the development of hand use during bimanual activities among children with unilateral cerebral palsy (CP). METHOD A cohort of 166 children (79 females, 87 males; age range 18mo-13y, mean [SD] age at first assessment 37.6mo [20.5mo]) with unilateral CP, registered in the Norwegian CP Follow-up Program with two or more Assisting Hand Assessments (AHAs), were included in this longitudinal study comprising 524 AHAs. Developmental limits and rates were estimated by non-linear mixed effects models and compared between a stable limit model (SLM) and a peak and decline model. Development was described according to Manual Ability Classification System (MACS) levels and AHA performance at 18 months of age (AHA-18). RESULTS Children in MACS level I, or in the high AHA-18 group, reached highest limits and had the most rapid development (p

Rheumatoid arthritis (RA) is a complex disease with a wide range of underlying susceptibility factors. Recently, dysregulation of microRNAs (miRNAs) in RA have been reported in several immune cell types from blood. However, B cells have not been studied in detail yet. Given the autoimmune nature of RA with the presence of autoantibodies, CD19+ B cells are a key cell type in RA pathogenesis and alterations in CD19+ B cell subpopulations have been observed in patient blood. Therefore, we aimed to reveal the global miRNA repertoire and to analyze miRNA expression profile differences in homogenous RA patient phenotypes in blood-derived CD19+ B cells. Small RNA sequencing was performed on CD19+ B cells of newly diagnosed untreated RA patients (n=10), successfully methotrexate (MTX) treated RA patients in remission (MTX treated RA patients, n=18) and healthy controls (n=9). The majority of miRNAs was detected across all phenotypes. However, significant expression differences between MTX treated RA patients and controls were observed for 27 miRNAs, while no significant differences were seen between the newly diagnosed patients and controls. Several of the differentially expressed miRNAs were previously found to be dysregulated in RA including miR-223-3p, miR-486-3p and miR-23a-3p. MiRNA target enrichment analysis, using the differentially expressed miRNAs and miRNA-target interactions from miRTarBase as input, revealed enriched target genes known to play important roles in B cell activation, differentiation and B cell receptor signaling, such as STAT3, PRDM1 and PTEN. Interestingly, many of those genes showed a high degree of correlated expression in CD19+ B cells in contrast to other immune cell types. Our results suggest important regulatory functions of miRNAs in blood-derived CD19+ B cells of MTX treated RA patients and motivate for future studies investigating the interactive mechanisms between miRNA and gene targets, as well as the possible predictive power of miRNAs for RA treatment response.

1Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples, which may indicate that these genes are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Seq data, which are generally normalized to remove technical variability. Here, we find and demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes, and that this can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular when preprocessing large-scale heterogeneous data, quantile-based normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. We therefore developed CAIMAN, a method to correct for false-positive associations that may arise from normalization of RNA-Seq data. CAIMAN utilizes a Gaussian mixture model to fit the distribution of gene expression and to adaptively select the threshold to define lowly expressed genes, which are prone to form false-positive associations. Thereafter, CAIMAN corrects the normalized expression for these genes by removing the variability across samples that might lead to false-positive associations. Moreover, CAIMAN avoids arbitrary gene filtering and retains associations to genes that only express in small subgroups of samples, highlighting its potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data.

Context Lifestyle interventions have not efficaciously reduced complications caused by maternal weight on fetal growth, requiring insight into explanatory mediators. Objective We hypothesized that maternal mediators, including adiponectin, leptin, insulin, and glucose, mediate effects of pregestational BMI (pBMI) and gestational weight gain (GWG) on birthweight and neonatal fat mass percentage (FM%) through placental weight and fetal mediators, including insulin levels (Ifv) and venous-arterial glucose difference (ΔGfva). Hypothesized confounders were maternal age, gestational age, and parity. Methods A cross-sectional study of healthy mother-offspring-pairs (n = 165) applying the 4-vessel in vivo sampling method at Oslo University Hospital, Norway. We obtained pBMI, GWG, birthweight, and placental weight. FM% was available and calculated for a subcohort (n = 84). We measured circulating levels of adiponectin, leptin, glucose, and insulin and performed path analysis and traditional mediation analyses based on linear regression models. Results The total effect of pBMI and GWG on newborn size was estimated to be 30 g (range, 16-45 g) birthweight and 0.17 FM% (range, 0.04-0.29 FM%) per kg∙m–2 pBMI and 31 g (range, 18-44 g) and 0.24 FM% (range, 0.10-0.37 FM%) per kg GWG. The placental weight was the main mediator, mediating 25-g birthweight and 0.11 FM% per kg∙m–2 pBMI and 25-g birthweight and 0.13 FM% per kg GWG. The maternal mediators mediated a smaller part of the effect of pBMI (3.8-g birthweight and 0.023 FM% per kg∙m–2 pBMI) but not GWG. Conclusion Placental weight was the main mediator linking pBMI and GWG to birthweight and FM%. The effect of pBMI, but not GWG, on birthweight and FM%, was also mediated via the maternal and fetal mediators.

Background More accurate risk assessments are needed to improve prostate cancer management. Objective To identify blood-based protein biomarkers that provided prognostic information for risk stratification. Design, setting, and participants Mass spectrometry was used to identify biomarker candidates from blood, and validation studies were performed in four independent cohorts retrospectively collected between 1988 and 2015. Outcome measurements and statistical analysis The primary outcome objectives were progression-free survival, prostate cancer–specific survival (PCSS), and overall survival. Statistical analyses to assess survival and model performance were performed. Results and limitation Serum leucine-rich α-2-glycoprotein 1 (LRG1) was found to be elevated in fatal prostate cancer. LRG1 provided prognostic information independent of metastasis and increased the accuracy in predicting PCSS, particularly in the first 3 yr. A high LRG1 level is associated with an average of two-fold higher risk of disease-progression and mortality in both high-risk and metastatic patients. However, our study design, with a retrospective analysis of samples spanning several decades back, limits the assessment of the clinical utility of LRG1 in today’s clinical practice. Thus, independent prospective studies are needed to establish LRG1 as a clinically useful biomarker for patient management. Conclusions High blood levels of LRG1 are unfavourable in newly diagnosed high-risk and metastatic prostate cancer, and LRG1 increased the accuracy of risk stratification of prostate cancer patients. Patient summary High blood levels of leucine-rich α-2-glycoprotein 1 are unfavourable in newly diagnosed high-risk and metastatic prostate cancer., Take Home Message High blood levels of leucine-rich α-2-glycoprotein 1 (LRG1) is unfavourable in patients with newly diagnosed high-risk or metastatic prostate cancer. Implementation of a blood test for LRG1 alongside standard risk stratification schemes could provide higher precision in treatment decisions.

IntroductionTo better understand the development of post-stroke cognitive impairment,we explored the association between pre-stroke neuroimaging features and pre-stroke cognitive impairment and investigated possible gender differences. Few previous studies on this topic have been performed.MethodsIn this large prospective longitudinal multicenter brain-MRI cohort study, patients admitted to five stroke units at five different Norwegian hospitals were recruited as part of the Norwegian cognitive impairment after stroke study. Visual radiological assessment of small vessel disease and neurodegenerative changes were performed on brain MRI from 410 patients. Pre-stroke cognition was assessed using the Global Deterioration Scale.ResultsAt least one pathological marker was found in 68% of the patients. The mean age (SD) of the patients with no pathological changes other than the acute stroke, was 70 (± 12.9) and 75 (± 10.2) years for those with pathological scans (p ≤ 0.001). Men were more likely to have at least one pathological brain MRI finding, lacunes, or pathological medial temporal lobe atrophy. The highest percentage of patients with a pathological pre-stroke GDS were found in the “cerebrovascular pathology” group (37.5%) and in the “mixed pathology”-group (44%). In these groups, both men and women had an increased risk of impaired pre-stroke cognition.ConclusionThe majority of patients showed preexisting structural brain pathology. Cerebrovascular pathology was the dominating imaging finding associated with cognitive impairment, thus indicating that the pathogenesis of pre-stroke cognitive impairment might be driven more by small vessel disease (SVD) than neurodegenerative changes. Gender differences exists, with less pathology in women.

ObjectiveThe role of neutrophil extracellular traps (NETs) in acute heart failure is unknown. We recently showed that interleukin 8, a putative NETs stimulator, was associated with myocardial recovery in acute heart failure complicating ST-elevation myocardial infarction (STEMI). In this exploratory post-hoc study, we aimed to investigate the role of NETs components in relation to myocardial function and interleukin 8 in STEMI patients with symptomatic acute heart failure.MethodsIn 61 STEMI patients developing acute heart failure within 48 hours of successful revascularization, wall motion score index (WMSI), global longitudinal strain (GLS) and left ventricular ejection fraction (LVEF) were assessed by echocardiography at baseline and on day 5. Blood drawn at baseline and days 1, 2 and 5 was used to quantify double-stranded DNA (dsDNA), myeloperoxidase-DNA complexes (MPO-DNA) and citrullinated histone 3 (CitH3). The area under the curve (AUC) of each NETs marker and interleukin 8 was approximated for the first 5 days.ResultsdsDNAAUC and MPO-DNAAUC correlated significantly with change in WMSI from baseline to day 5 (rs = 0.28 for both, p≤0.05), whereas NETs AUCs did not correlate with changes in GLS and LVEF. dsDNAAUC was significantly correlated with interleukin 8AUC (r = 0.40, p = 0.003). However, mixed model regression could not identify a significant effect of the NETs components on myocardial function parameters.ConclusionsIn this cohort with acute heart failure complicating STEMI, NETs components were partly correlated with myocardial function and interleukin 8 levels, yet no causal relationship between NETs components and myocardial recovery could be established.Clinical trial registrationClinicalTrials.gov, identifier: NCT00324766.

Background Keratoacanthoma (KA) has a unique life cycle of rapid growth and spontaneous regression that shows similarities to the hair follicle cycle, which involves an active Wnt signaling during physiological regeneration. We analyzed the expression of the Wnt signaling proteins β-catenin, Lef1, Sox9, and Cyclin D1 in young and old human KAs to investigate a possible role for Wnt signaling in KAs. Aim To investigate the role of the Wnt/β-catenin signaling pathway in human KAs. Methods and results Formalin-fixed, paraffin-embedded tissue samples of 67 KAs were analyzed for protein expression using immunohistochemistry. The majority of KAs were positive for Sox9 and Cyclin D1 but not for nuclear-localized β-catenin or Lef-1. No significant differences in protein expressions were seen between young and old KAs. However, we found a significant association between Ki67 and Cyclin D1 proteins (P= .008). Conclusions The Wnt signaling pathway does not appear to play a significant role in the biogenesis of human KA. Sox9 overexpression may be indicative of inhibition of Wnt signaling. Sox-9 and Cyclin D1 are proliferation markers that are most likely transactivated by alternate signaling pathways.

High-throughput sequencing has emerged as the favoured method to study microRNA (miRNA) expression, but biases introduced during library preparation have been reported. To assist researchers choose the most appropriate library preparation kit, we recently compared the performance of six commercially-available kits on synthetic miRNAs and human RNA, where library preparation was performed by the vendors. We hereby supplement this study with data from two further commonly used kits (NEBNext, NEXTflex) whose manufacturers initially declined to participate. As before, performance was assessed with respect to sensitivity, reliability, titration response and differential expression. Despite NEXTflex employing partially-randomised adapter sequences to minimise bias, we reaffirm that biases in miRNA abundance are kit-specific, complicating the comparison of miRNA datasets generated using different kits.

Objective: To evaluate the effect of a dialogue-based intervention targeting psychosocial well-being at 12 months post-stroke. Design: Multicenter, prospective, randomized, assessor-blinded, controlled trial with two parallel groups. Setting: Community. Subjects: Three-hundred and twenty-two adults (⩾18 years) with stroke within the last four weeks were randomly allocated into intervention group (n = 166) or control group (n = 156). Interventions: The intervention group received a dialogue-based intervention to promote psychosocial well-being, comprising eight individual 1–1½ hour sessions delivered during the first six months post-stroke. Main measures: The primary outcome measure was the General Health Questionnaire-28 (GHQ-28). Secondary outcome measures included the Stroke and Aphasia Quality of Life Scale-39g, the Sense of Coherence scale, and the Yale Brown single-item questionnaire. Results: The mean (SD) age of the participants was 66.8 (12.1) years in the intervention group and 65.7 (13.3) years in the control group. At 12 months post-stroke, the mean (SE) GHQ-28 score was 20.6 (0.84) in the intervention group and 19.9 (0.85) in the control group. There were no between-group differences in psychosocial well-being at 12 months post-stroke (mean difference: −0.74, 95% confidence interval (CI): −3.08, 1.60). The secondary outcomes showed no statistically significant between-group difference in health-related quality of life, sense of coherence, or depression at 12 months. Conclusion: The results of this trial did not demonstrate lower levels of emotional distress and anxiety or higher levels of health-related quality of life in the intervention group (dialogue-based intervention) as compared to the control group (usual care) at 12 months post-stroke. The following financial support was granted for the research, authorship, and/or publication of this article: the European Union Seventh Framework Program (FP7-PEOPLE-2013-COFUND; Grant Agreement No. 609020—Scientia Fellows); the South-Eastern Norway Regional Health Authority (Project no. 2013086); and the Extra Foundation (Grant No. 2015/FO13753). The University of Oslo, Oslo University Hospital, the Inland Norway University of Applied Sciences, and UiT, the Arctic University of Norway, Narvik have provided research time, administrative and organizational support, and additional funding for the study.

DNA methylation data-based precision cancer diagnostics is emerging as the state of the art for molecular tumor classification. Standards for choosing statistical methods with regard to well-calibrated probability estimates for these typically highly multiclass classification tasks are still lacking. To support this choice, we evaluated well-established machine learning (ML) classifiers including random forests (RFs), elastic net (ELNET), support vector machines (SVMs) and boosted trees in combination with post-processing algorithms and developed ML workflows that allow for unbiased class probability (CP) estimation. Calibrators included ridge-penalized multinomial logistic regression (MR) and Platt scaling by fitting logistic regression (LR) and Firth’s penalized LR. We compared these workflows on a recently published brain tumor 450k DNA methylation cohort of 2,801 samples with 91 diagnostic categories using a 5 × 5-fold nested cross-validation scheme and demonstrated their generalizability on external data from The Cancer Genome Atlas. ELNET was the top stand-alone classifier with the best calibration profiles. The best overall two-stage workflow was MR-calibrated SVM with linear kernels closely followed by ridge-calibrated tuned RF. For calibration, MR was the most effective regardless of the primary classifier. The protocols developed as a result of these comparisons provide valuable guidance on choosing ML workflows and their tuning to generate well-calibrated CP estimates for precision diagnostics using DNA methylation data. Computation times vary depending on the ML algorithm from

The molecular characterization of tumor samples by multiple omics data sets of different types or modalities (e.g. gene expression, mutation, CpG methylation) has become an invaluable source of information for assessing the expected performance of individual drugs and their combinations. Merging relevant information from the omics data modalities provides the statistical basis for determining suitable therapies for specific cancer patients. Different data modalities may each have their own specific structures that need to be taken into account during inference. In this paper, we assume that each omics data modality has a low-rank structure with only a few relevant features that affect the prediction and we propose to use a composite local nuclear norm penalization for learning drug sensitivity. Numerical results show that the composite low-rank structure can improve the prediction performance compared to using a global low-rank approach or elastic net regression.

Background An important feature in many genomic studies is quality control and normalization. This is particularly important when analyzing epigenetic data, where the process of obtaining measurements can be bias prone. The GAW20 data was from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN), a study with multigeneration families, where DNA cytosine-phosphate-guanine (CpG) methylation was measured pre- and posttreatment with fenofibrate. We performed quality control assessment of the GAW20 DNA methylation data, including normalization, assessment of batch effects and detection of sample swaps. Results We show that even after normalization, the GOLDN methylation data has systematic differences pre- and posttreatment. Through investigation of (a) CpGs sites containing a single nucleotide polymorphism, (b) the stability of breeding values for methylation across time points, and (c) autosomal gender-associated CpGs, 13 sample swaps were detected, 11 of which were posttreatment. Conclusions This paper demonstrates several ways to perform quality control of methylation data in the absence of raw data files and highlights the importance of normalization and quality control of the GAW20 methylation data from the GOLDN study.

Gonadotroph tumours are the most abundant of the clinically silent pituitary tumours. There is a lack of reliable prognostic markers predicting their clinical course. Our aim was to determine the level of E-cadherin and N-cadherin in a cohort of clinically silent gonadotroph pituitary tumours, and compare them to the rate of reintervention. Tumour tissue from primary surgery was retrospectively investigated and compared with clinical data. Immunohistochemical (N = 105) and real time-qPCR (N = 85) analyses for the levels of N-cadherin and the extra- and intracellular domains of E-cadherin were performed. The immunoreactive scores (IRS) and mRNA relative quantity were compared to the rate of reintervention. The tumours presented a high IRS for N-cadherin (Median 12 (IQR 12-12)) and almost no immunoreactivity for the extracellular domain of E-cadherin (Median 0 (IQR 0-0)). The membranous staining for the intracellular domain of E-cadherin varied (Median 6 (IQR 4-6). Reduced membranous expression of the intracellular domain of E-cadherin was associated with nuclear presence of the same domain. Nuclear staining for the intracellular domain of E-cadherin was associated with a lower rate of reintervention (p = 0.01). We found that silent gonadotroph tumours presented high IRS for N-cadherin and low IRS for the extracellular domain of E-cadherin. A substantial proportion of the tumours presented nuclear staining for the intracellular domain of E-cadherin, accompanied by a reduced membranous expression of the intracellular domain of E-cadherin. Absence of nuclear staining for the intracellular domain of E-cadherin served as an independent predictor of reintervention.

In the postgenome era, multiple types of molecular data for the same set of samples are often available and should be analyzed jointly in an integrative analysis in order to maximize the information gain. Bayesian methods are particularly well suited for integrating different biological data sources. In this article, we cover crucial tasks and corresponding methods with a focus on integrative analyses. We emphasize gene prioritization, model-based cluster approaches for subgroup identification, regression modeling, and prediction, as well as structure learning using network models. Our review introduces prior concepts for sparsity and variable selection and concludes with some aspects on validation and computation.

Chronic lymphocytic leukemia is a malignancy of mature B cells that strongly depend on microenvironmental factors, and their deprivatio n has been ide nt ified as a promising treatmen t approach for this incurable disease. Cytokine array screening of 247 chronic lymphocytic leukemia serum samples revealed elevated levels of tumor necrosis factor (TNF) receptor-1 which were associated with poor clinical outcome. We detected a microenvironment-induced expression of TNF receptor-1 in chronic lymphocytic leukemia cells in vitro, and an aberrantly high expression of this receptor in the proliferation centers of patients’ lymph nodes. Stimulation of TNF receptor-1 with TNF-α enhanced nuclear factor κ-light-chain-enhancer of activated B cells (NFκB) activity and viability of chronic lymphocytic leukemia cells, which was inhibited by wogonin. The therapeutic effects of wogonin were analyzed in mice after adoptive transfer of Em-T-cell leukemia 1 (TCL1) leukemic cells. Wogonin treatment prevented leukemia development when given early after transplantation. The treatment of fullblown leukemia resulted in the loss of the TNF receptor-1 on chronic lymphocytic leukemia cells and their mobilization to blood. Targeting TNF receptor-1 signaling is therefore proposed for the treatment of chronic lymphocytic leukemia.

B cell receptor (BCR) signaling is a key for survival of chronic lymphocytic leukemia (CLL) cells, and BCR signaling inhibitors are clinically active. However, relapse and resistance to treatment require novel treatment options. To detect novel candidate therapeutic targets, we performed a genome-wide DNA methylation screen with custom arrays and identified aberrant promoter DNA methylation in 2,192 genes. The transcription factor NFATC1 that is a downstream effector of BCR signaling was among the top hypomethylated genes and was concomitantly transcriptionally upregulated in CLL. Intriguingly, NFATC1 promoter DNA hypomethylation levels were significantly variant in clinical trial cohorts from different disease progression stages and furthermore correlated with Binet disease staging and thymidine kinase levels, strongly suggesting a central role of NFATC1 in CLL development. Functionally, DNA hypomethylation at NFATC1 promoter inversely correlated with RNA levels of NFATC1 and dysregulation correlated with expression of target genes BCL-2, CCND1 and CCR7. The inhibition of the NFAT regulator calcineurin with tacrolimus and cyclosporin A and the BCR signaling inhibitor ibrutinib significantly reduced NFAT activity in leukemic cell lines, and NFAT inhibition resulted in increased apoptosis of primary CLL cells. In summary, our results indicate that the aberrant activation of NFATC1 by DNA hypomethylation and BCR signaling plays a major role in the pathomechanism of CLL.

Nonfunctioning pituitary adenomas (NFPAs) are fairly common and require a multidisciplinary approach. Reliable markers of a clinically aggressive course are lacking. Medical treatment is not available, and transsphenoidal surgery is the preferred primary treatment.We aimed to characterize the somatostatin, estrogen, and progesterone receptor distribution for NFPAs and compare it with factors of tumor aggressiveness.Tumor samples for immunohistochemistry (n = 145) and quantitative reverse transcription polymerase chain reaction (n = 106) analyses of somatostatin receptor (SSTR) 1, SSTR2, SSTR3, SSTR5, estrogen receptor α (ERα), and progesterone receptor (PR) were measured by immunoreactive score (IRS) and messenger RNA relative quantity and retrospectively compared with variables of aggressiveness.All patients were operated at the same tertiary referral center.A total of 164 patients with NFPA and tumor tissue from the primary operation were included.SSTR3 was expressed abundantly by immunohistochemistry in all NFPAs. The IRS of ERα correlated with that of SSTR2 in male patients only (males, P0.001; females, P = 0.8). Low ERα level was linked to a higher reintervention rate (P = 0.001) and earlier reintervention (P = 0.004) in male patients only (females, P = 0.95 and P = 0.65, respectively). Absence of ERα together with age provided a good prediction model for reintervention in male patients with gonadotroph adenomas.SSTR3 is expressed abundantly in NFPAs and is therefore a possible target for medical treatment. Absence of ERα together with young age may predict tumor recurrence in groups of NFPAs. Further validation in systematic prospective studies is needed.

Non-functioning pituitary adenomas are common, and the treatment and follow-up of these patients represent a multidisciplinary challenge. First line treatment is transphenoidal surgery, with debulking or total removal of tumour. A substantial portion of the tumours relapse after surgery, and there is no consensus of how to follow these patients postoperatively. Our aim was to characterize the postoperative growth of non-functioning pituitary adenomas and correlate it to clinical and paraclinical data. We retrospectively registered 52 patients operated for non-functioning pituitary adenomas, with four or more consecutive MR-investigations not interrupted by secondary treatment. Adenoma volumes were estimated by the Cavalieri principle with summation of manually drawn areas multiplied by slice interval. Growth curves were modelled and tumour volume doubling time was calculated for 39 tumours with regrowth after surgery. A total of 13 tumours showed exponential growth, 10 linear growth and 16 logistic growth after surgery. The remaining 13 did not show regrowth of tumour. Seven of the exponential growing tumours underwent secondary surgery, compared to one and two of linear and logistic growing tumours (p = 0.03), respectively. Initial tumour volume doubling time was significantly lower in logistic growing tumours than in exponential growing tumours (p

Bayesian variable selection becomes more and more important in statistical analyses, in particular when performing variable selection in high dimensions. For survival time models and in the presence of genomic data, the state of the art is still quite unexploited. One of the more recent approaches suggests a Bayesian semiparametric proportional hazards model for right censored time-to-event data. We extend this model to directly include variable selection, based on a stochastic search procedure within a Markov chain Monte Carlo sampler for inference. This equips us with an intuitive and flexible approach and provides a way for integrating additional data sources and further extensions. We make use of the possibility of implementing parallel tempering to help improve the mixing of the Markov chains. In our examples, we use this Bayesian approach to integrate copy number variation data into a gene-expression-based survival prediction model. This is achieved by formulating an informed prior based on copy number variation. We perform a simulation study to investigate the model’s behavior and prediction performance in different situations before applying it to a dataset of glioblastoma patients and evaluating the biological relevance of the findings.

Background Several studies have documented the variety of post-stroke psychosocial challenges, which are complex, multifaceted, and affect a patient’s rehabilitation and recovery. Due to the consequences of these challenges, psychosocial well-being should be considered an important outcome of the stroke rehabilitation. Thus, a valid and reliable instrument that is appropriate for the stroke population is required. The factor structure of the Norwegian version of GHQ-28 has not previously been examined when applied to a stroke population. The purpose of this study was to explore the psychometric properties of the GHQ-28 when applied in the stroke population included in the randomized controlled trial; “Psychosocial well-being following stroke”, by evaluating the internal consistency, exploring the factor structure, construct validity and measurement invariance. Methods Data were obtained from 322 individuals with a stroke onset within the past month. The Kaiser-Meyer-Olkin (KMO) test was used to test the sampling adequacy for exploratory factor analysis, and the Bartlett’s test of sphericity was used to test equal variances. Internal consistency was analysed using Cronbach’s alpha. The factor structure of the GHQ-28 was evaluated by exploratory factor analysis (EFA), and a confirmatory factor analysis (CFA) was used to determine the goodness of fit to the original structure of the outcome measurement. Measurement invariance for two time points was evaluated by configural, metric and scalar invariance. Results The results from the EFA supported the four-factor dimensionality, but some of the items were loaded on different factors compared to those of the original structure. The differences resulted in a reduced goodness of fit in the CFA. Measurement invariance at two time points was confirmed. Conclusions The change in mean score from one to six months on the GHQ-28 and the factor composition are assumed to be affected by characteristics in the stroke population. The results, when applying the GHQ-28 in a stroke population, and sub-factor analysis based on the original factor structure should be interpreted with caution. Trial registration ClinicalTrials.gov, NCT02338869 , registered 10/04/2014.

DNA methylation data-based precision cancer diagnostics is emerging as the state of the art for molecular tumor classification. Standards for choosing statistical methods with regard to well-calibrated probability estimates for these typically highly multiclass classification tasks are still lacking. To support this choice, we evaluated well-established machine learning (ML) classifiers including random forests (RFs), elastic net (ELNET), support vector machines (SVMs) and boosted trees in combination with post-processing algorithms and developed ML workflows that allow for unbiased class probability (CP) estimation. Calibrators included ridge-penalized multinomial logistic regression (MR) and Platt scaling by fitting logistic regression (LR) and Firth's penalized LR. We compared these workflows on a recently published brain tumor 450k DNA methylation cohort of 2,801 samples with 91 diagnostic categories using a 5 × 5-fold nested cross-validation scheme and demonstrated their generalizability on external data from The Cancer Genome Atlas. ELNET was the top stand-alone classifier with the best calibration profiles. The best overall two-stage workflow was MR-calibrated SVM with linear kernels closely followed by ridge-calibrated tuned RF. For calibration, MR was the most effective regardless of the primary classifier. The protocols developed as a result of these comparisons provide valuable guidance on choosing ML workflows and their tuning to generate well-calibrated CP estimates for precision diagnostics using DNA methylation data. Computation times vary depending on the ML algorithm from15 min to 5 d using multi-core desktop PCs. Detailed scripts in the open-source R language are freely available on GitHub, targeting users with intermediate experience in bioinformatics and statistics and using R with Bioconductor extensions.

Large-scale {\it in vitro} drug sensitivity screens are an important tool in personalized oncology to predict the effectiveness of potential cancer drugs. The prediction of the sensitivity of cancer cell lines to a panel of drugs is a multivariate regression problem with high-dimensional heterogeneous multi-omics data as input data and with potentially strong correlations between the outcome variables which represent the sensitivity to the different drugs. We propose a joint penalized regression approach with structured penalty terms which allow us to utilize the correlation structure between drugs with group-lasso-type penalties and at the same time address the heterogeneity between omics data sources by introducing data-source-specific penalty factors to penalize different data sources differently. By combining integrative penalty factors (IPF) with tree-guided group lasso, we create the IPF-tree-lasso method. We present a unified framework to transform more general IPF-type methods to the original penalized method. Because the structured penalty terms have multiple parameters, we demonstrate how the interval-search Efficient Parameter Selection via Global Optimization (EPSGO) algorithm can be used to optimize multiple penalty parameters efficiently. Simulation studies show that IPF-tree-lasso can improve the prediction performance compared to other lasso-type methods, in particular for heterogenous data sources. Finally, we employ the new methods to analyse data from the Genomics of Drug Sensitivity in Cancer project., Comment: Zhao Z, Zucknick M (2020). Structured penalized regression for drug sensitivity prediction. Journal of the Royal Statistical Society, Series C. 19 pages, 6 figures and 2 tables

Introduction and hypothesis In Palestine, episiotomy is frequently used among primiparous women.This study assesses the effect of training birth attendants in applying bimanual perineal support during delivery by either animated instruction on tablets or hands-on training on episiotomy rates among primiparous women. Methods An interventional cohort study was performed from 15 October 2015 to 31 January 2017, including all primiparous women with singletons and noninstrumental vaginal deliveries at six Palestinian hospitals. Intervention 1 (animated instructions on tablets) was conducted in Hospitals 1, 2, 3, and 4. Intervention 2 (bedside hands-on training) was applied in Hospitals 1 and 2 only. Hospitals 5 and 6 did not receive interventions. Differences in episiotomy rates in intervention and nonintervention hospitals were assessed before and after the interventions and presented as p values using chi-square test, and odds ratios (OR) with 95% confidence intervals (CI). Differences in the demographic and obstetric characteristics were presented as p values using the Kruskal–Wallis test. Results Of 46,709 women, 12,841 were included. The overall episiotomy rate in the intervention hospitals did not change significantly after intervention 1, from 63.1 to 62.1% (OR = 0.96, 95% CI 0.84–1.08), but did so after intervention 2, from 61.1 to 38.1% (OR = 0.39, 95% CI 0.33–0.47). Rates after Intervention 2 changed from 65.0 to 47.3% (OR = 0.52, 95% CI 0.40–0.67) in Hospital 1 and from 39.4 to 25.1% (OR = 0.49, 95% CI 0.35–0.68) in Hospital 2. Conclusions Hands-on training of bimanual perineal support during delivery of primiparous women was significantly more effective in reducing episiotomy rates than animated instruction videos alone. Electronic supplementary material The online version of this article (10.1007/s00192-018-3711-6) contains supplementary material, which is available to authorized users.

Objective: To evaluate the effect of a dialogue-based intervention on psychosocial well-being 6 months after stroke. Design: Multicentre, prospective, randomized controlled trial. Subjects: Adults (aged ≥ 18 years) who had their first or recurrent stroke within the last month, were medically stable, had sufficient cognitive functioning to participate and understood and spoke Norwegian. Methods: A total of 322 participants were randomly assigned to the intervention (n = 166) or control (n = 156) group. Participants in the intervention group received up to 8 individual sessions aimed at supporting the coping and life skills of stroke survivors in addition to usual care. The primary outcome was the proportion of participants with normal mood measured by the General Health Questionnaire-28 (GHQ-28). The secondary outcomes included health-related quality of life (Stroke and Aphasia Quality of Life Scale; SAQOL-39g), depression (Yale-Brown single-item questionnaire; Yale) and sense of coherence (SOC-13). Results: After controlling for the baseline values, no significant benefit was found in the intervention group over the control group (odds ratio (OR): 0.898: 95% confidence interval (95% CI): 0.54-1.50, p = 0.680) 6 months post-stroke. Conclusion: Psychosocial well-being improved during the first 6 months after stroke in both arms of the trial, but no statistically significant benefit of the dialogue-based intervention was found compared with usual care. Lay Abstract The aim of this study was to evaluate the effect of a dialogue-based intervention on psychosocial well-being 6 months after stroke. A total of 322 participants were assigned to an intervention (= 166) or control (= 156) group. Participants in the intervention group received up to 8 individual sessions aimed at supporting the coping and life skills of stroke survivors in addition to usual care. Psychosocial well-being improved during the first 6 months after the stroke in both arms of the trial. However, no benefit of the dialogue-based intervention was found compared with usual care. The research leading to these results received funding from the European Union Seventh Framework Programme (FP7-PEOPLE-2013-COFUND) under grant agreement no. 609020 - Scientia Fellows (LKB), the South-Eastern Norway Regional Health Authority (Project number 2013086) (US, LKB) and the Extra Foundation (2015/FO13753) (EGH).

Chronic lymphocytic leukemia (CLL) is known for its strong dependency on the tumor microenvironment. We found progranulin (GRN), a protein that has been linked to inflammation and cancer, to be upregulated in the serum of CLL patients compared to healthy controls, and increased GRN levels to be associated with an increased hazard for disease progression and death. This raised the question of whether GRN is a functional driver of CLL. We observed that recombinant GRN did not directly affect viability, activation, or proliferation of primary CLL cells in vitro. However, GRN secretion was induced in co-cultures of CLL cells with stromal cells that enhanced CLL cell survival. Gene expression profiling and protein analyses revealed that primary mesenchymal stromal cells (MSCs) in co-culture with CLL cells acquire a cancer-associated fibroblast-like phenotype. Despite its upregulation in the co-cultures, GRN treatment of MSCs did not mimic this effect. To test the relevance of GRN for CLL in vivo, we made use of the E&mu, TCL1 CLL mouse model. As we detected strong GRN expression in myeloid cells, we performed adoptive transfer of E&mu, TCL1 leukemia cells to bone marrow chimeric Grn&minus, /&minus, mice that lack GRN in hematopoietic cells. Thereby, we observed that CLL-like disease developed comparable in Grn&minus, chimeras and respective control mice. In conclusion, serum GRN is found to be strongly upregulated in CLL, which indicates potential use as a prognostic marker, but there is no evidence that elevated GRN functionally drives the disease.

Background Keratoacanthoma (KA) is a common keratinocytic skin neoplasm that typically develops rapidly and undergoes complete spontaneous regression. As the pro-apoptotic p53 protein may be involved in the lifecycle of KA, we studied the p53 status throughout the main stages of KA that include proliferation, maturation and regression in a large series of lesions. Methods One-hundred and twenty-four KAs were characterized with respect to age of the lesions both clinically and histopathologically, in addition to phenotypic characteristics such as cellular atypia, infiltration, inflammation and fibrosis. Tp53 mutations were detected by capillary electrophoresis, and p53 protein levels were assessed by immunohistochemistry. Results Tp53 mutations were detected in 49 cases (39.5%) and were associated with high p53 protein levels (p = 0.007) and histopathologic age of the lesions (p = 0.044). Significant association was also seen between high p53 protein levels and atypia (p = 0.036), whereas the association with infiltration showed borderline significance (p = 0.057). High p53 protein levels were significantly associated with gene mutations in transplanted, but not in non-transplanted patients. Conclusion We show a high frequency of Tp53 mutations in KAs that is associated with increased p53 levels. The results indicate a role for the p53 protein in KA development.

MGMT promoter methylation status is currently the only established molecular prognosticator in IDH wild-type glioblastoma multiforme (GBM). Therefore, we aimed to discover novel therapy-associated epigenetic biomarkers. After enrichment for hypermethylated fractions using methyl-CpG-immunoprecipitation (MCIp), we performed global DNA methylation profiling for 14 long-term (LTS; >36 months) and 15 short-term (STS; 6-10 months) surviving GBM patients. Even after exclusion of the G-CIMP phenotype, we observed marked differences between the LTS and STS methylome. A total of 1,247 probes in 706 genes were hypermethylated in LTS and 463 probes in 305 genes were found to be hypermethylated in STS patients (p values