Efficacy and safety of filgotinib in methotrexate-naive patients with rheumatoid arthritis with poor prognostic factors: post hoc analysis of FINCH 3

OBJECTIVE: This analysis evaluated efficacy and safety of filgotinib, a Janus-associated kinase 1-preferential inhibitor, in methotrexate (MTX)-naive patients with rheumatoid arthritis (RA) with multiple poor prognostic factors (PPFs). METHODS: This was a post hoc analysis of the phase III, randomised, double-blind, active-controlled, FINCH 3 study (clinicaltrials.gov NCT02886728). Patients received once-daily oral filgotinib 200 or 100 mg plus once-weekly oral MTX ≤20 mg (FIL200 + MTX and FIL100 + MTX), filgotinib 200 mg monotherapy (FIL200), or oral MTX monotherapy (MTX-mono) for up to 52 weeks. PPFs investigated were seropositivity for rheumatoid factor or anticyclic citrullinated peptide antibodies, high-sensitivity C reactive protein (CRP) ≥4 mg/L, Disease Activity Score in 28 joints with CRP (DAS28(CRP)) >5.1, and presence of erosions. Filgotinib efficacy and safety in patients with all four PPFs at baseline were explored versus MTX-mono within this subgroup and compared informally with the overall population. RESULTS: Of 1249 patients in FINCH 3, 510 (40.8%) had all PPFs. Efficacy of FIL200 + MTX among these patients was comparable to the overall population, with higher rates of 20%/50%/70% improvement from baseline by American College of Rheumatology criteria, DAS28(CRP) <2.6, and remission; greater improvement in physical function and pain; and better inhibition of structural damage relative to MTX-mono. FIL100 + MTX and FIL200 were not consistently more efficacious versus MTX-mono. Safety of filgotinib in patients with PPFs was comparable to the overall population; no new safety signals were observed. CONCLUSION: FIL200 + MTX efficacy and safety in patients with multiple PPFs were similar to the overall population

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance

Consensus Analysis of Whole Transcriptome Profiles from Two Breast Cancer Patient Cohorts Reveals Long Non-Coding RNAs Associated with Intrinsic Subtype and the Tumour Microenvironment.

Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes and diseases such as cancer; however, their functions remain poorly characterised. Several studies have demonstrated that lncRNAs are typically disease and tumour subtype specific, particularly in breast cancer where lncRNA expression alone is sufficient to discriminate samples based on hormone status and molecular intrinsic subtype. However, little attempt has been made to assess the reproducibility of lncRNA signatures across more than one dataset. In this work, we derive consensus lncRNA signatures indicative of breast cancer subtype based on two clinical RNA-Seq datasets: the Utah Breast Cancer Study and The Cancer Genome Atlas, through integration of differential expression and hypothesis-free clustering analyses. The most consistent signature is associated with breast cancers of the basal-like subtype, leading us to generate a putative set of six lncRNA basal-like breast cancer markers, at least two of which may have a role in cis-regulation of known poor prognosis markers. Through in silico functional characterization of individual signatures and integration of expression data from pre-clinical cancer models, we discover that discordance between signatures derived from different clinical cohorts can arise from the strong influence of non-cancerous cells in tumour samples. As a consequence, we identify nine lncRNAs putatively associated with breast cancer associated fibroblasts, or the immune response. Overall, our study establishes the confounding effects of tumour purity on lncRNA signature derivation, and generates several novel hypotheses on the role of lncRNAs in basal-like breast cancers and the tumour microenvironment

Development of multivariable models to predict change in Body Mass Index within a clinical trial population of psychotic individuals

Many antipsychotics promote weight gain, which can lead to non-compliance and relapse of psychosis. By developing models that accurately identify individuals at greater risk of weight gain, clinicians can make informed treatment decisions and target intervention measures. We examined clinical, genetic and expression data for 284 individuals with psychosis derived from a previously published randomised controlled trial (IMPACT). These data were used to develop regression and classification models predicting change in Body Mass Index (BMI) over one year. Clinical predictors included demographics, anthropometrics, cardiac and blood measures, diet and exercise, physical and mental health, medication and BMI outcome measures. We included genetic polygenic risk scores (PRS) for schizophrenia, bipolar disorder, BMI, waist-hip-ratio, insulin resistance and height, as well as gene co-expression modules generated by Weighted Gene Co-expression Network Analysis (WGCNA). The best performing predictive models for BMI and BMI gain after one year used clinical data only, which suggests expression and genetic data do not improve prediction in this cohort

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Management of Asymptomatic Sporadic Nonfunctioning Pancreatic Neuroendocrine Neoplasms (ASPEN) ≤2 cm: Study Protocol for a Prospective Observational Study

Introduction: The optimal treatment for small, asymptomatic, nonfunctioning pancreatic neuroendocrine neoplasms (NF-PanNEN) is still controversial. European Neuroendocrine Tumor Society (ENETS) guidelines recommend a watchful strategy for asymptomatic NF-PanNEN 18 years, the presence of asymptomatic sporadic NF-PanNEN ≤2 cm proven by a positive fine-needle aspiration (FNA) or by the presence of a measurable nodule on high-quality imaging techniques that is positive at 68Gallium DOTATOC-PET scan. Conclusion: The ASPEN study is designed to investigate if an active surveillance of asymptomatic NF-PanNEN ≤2 cm is safe as compared to surgical approach.info:eu-repo/semantics/publishedVersio

Management of Asymptomatic Sporadic Non-Functioning Pancreatic Neuroendocrine Neoplasms No Larger Than 2 Cm: Interim Analysis of Prospective ASPEN Trial

info:eu-repo/semantics/publishedVersio