Neurofilament light in plasma is a potential biomarker of central nervous system involvement in systemic lupus erythematosus

BACKGROUND: Neuropsychiatric manifestations (NP) are common in systemic lupus erythematosus (SLE). However, the pathophysiological mechanisms are not completely understood. Neurofilament light protein (NfL) is part of the neuronal cytoskeleton. Increased NfL concentrations, reflecting neurodegeneration, is observed in cerebrospinal fluid (CSF) in several neurodegenerative and neuroinflammatory conditions. We aimed to explore if plasma NfL could serve as a biomarker for central nervous system (CNS) involvement in SLE. METHODS: Sixty-seven patients with SLE underwent neurological examination; 52 underwent lumbar puncture, while 62 underwent cerebral magnetic resonance imaging (MRI). We measured selected auto-antibodies and other laboratory variables postulated to have roles in NP pathophysiology in the blood and/or CSF. We used SPM12 software for MRI voxel-based morphometry. RESULTS: Age-adjusted linear regression analyses revealed increased plasma NfL concentrations with increasing creatinine (β = 0.01, p < 0.001) and Q-albumin (β = 0.07, p = 0.008). We observed higher plasma NfL concentrations in patients with a history of seizures (β = 0.57, p = 0.014), impaired motor function (β = 0.36, p = 0.008), increasing disease activity (β = 0.04, p = 0.008), and organ damage (β = 0.10, p = 0.002). Voxel-based morphometry suggested an association between increasing plasma NfL concentrations and the loss of cerebral white matter in the corpus callosum and hippocampal gray matter. CONCLUSION: Increased plasma NfL concentrations were associated with some abnormal neurological, cognitive, and neuroimaging findings. However, plasma NfL was also influenced by other factors, such as damage accrual, creatinine, and Q-albumin, thereby obscuring the interpretation of how plasma NfL reflects CNS involvement. Taken together, NfL in CSF seems a better marker of neuronal injury than plasma NfL in patients with SLE

Total late effect burden in long-term lymphoma survivors after high-dose therapy with autologous stem-cell transplant and its effect on health-related quality of life

Lymphoma survivors after high-dose therapy with autologous stem-cell transplant (HDT-ASCT) are at risk of several late effects, which might impair their health-related quality of life (HRQoL). We assessed the total late effect burden in this population, and how it affects HRQoL. All lymphoma survivors treated with HDT-ASCT as adults in Norway between 1987 and 2008 were identified, and 271 (68%) attended both a comprehensive clinical assessment and completed a questionnaire. Severity of 45 conditions in 12 organ-system categories were graded as mild, moderate, severe or life-threatening, according to a modified version of CTCAEv4.03. At a median of 8 years after HDT-ASCT, 98% of survivors had at least one moderate or more severe late effect and 56% had severe or life-threatening late effects. Fourteen percent had low, 39% medium and 47% high late effect burden, defined as having moderate or more severe late effects in 0-1, 2-3 and >3 organsystems, respectively. Female sex, increasing age, B-symptoms at diagnosis and >1 treatment line prior to HDT-ASCT were independently associated with having high late effect burden. The survivors had significantly poorer physical and mental HRQoL assessed by the Short Form-36 compared to age- and sex-matched controls. The prevalence of poor physical and mental HRQoL increased with higher late effect burden (both P<0.001), and the low burden group had better physical HRQoL than controls (P<0.001). In conclusion, lymphoma survivors after HDT-ASCT have impaired HRQoL, seemingly driven by a high late effect burden. This highlights the importance of prevention, regular assessments for early detection and treatment of late effects and modifiable risk factors

Molecular diagnosis of Burkitt\u27s lymphoma.

BACKGROUND: The distinction between Burkitt\u27s lymphoma and diffuse large-B-cell lymphoma is crucial because these two types of lymphoma require different treatments. We examined whether gene-expression profiling could reliably distinguish Burkitt\u27s lymphoma from diffuse large-B-cell lymphoma. METHODS: Tumor-biopsy specimens from 303 patients with aggressive lymphomas were profiled for gene expression and were also classified according to morphology, immunohistochemistry, and detection of the t(8;14) c-myc translocation. RESULTS: A classifier based on gene expression correctly identified all 25 pathologically verified cases of classic Burkitt\u27s lymphoma. Burkitt\u27s lymphoma was readily distinguished from diffuse large-B-cell lymphoma by the high level of expression of c-myc target genes, the expression of a subgroup of germinal-center B-cell genes, and the low level of expression of major-histocompatibility-complex class I genes and nuclear factor-kappaB target genes. Eight specimens with a pathological diagnosis of diffuse large-B-cell lymphoma had the typical gene-expression profile of Burkitt\u27s lymphoma, suggesting they represent cases of Burkitt\u27s lymphoma that are difficult to diagnose by current methods. Among 28 of the patients with a molecular diagnosis of Burkitt\u27s lymphoma, the overall survival was superior among those who had received intensive chemotherapy regimens instead of lower-dose regimens. CONCLUSIONS: Gene-expression profiling is an accurate, quantitative method for distinguishing Burkitt\u27s lymphoma from diffuse large-B-cell lymphoma

Stromal gene signatures in large-B-cell lymphomas.

BACKGROUND: The addition of rituximab to combination chemotherapy with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP), or R-CHOP, has significantly improved the survival of patients with diffuse large-B-cell lymphoma. Whether gene-expression signatures correlate with survival after treatment of diffuse large-B-cell lymphoma is unclear. METHODS: We profiled gene expression in pretreatment biopsy specimens from 181 patients with diffuse large-B-cell lymphoma who received CHOP and 233 patients with this disease who received R-CHOP. A multivariate gene-expression-based survival-predictor model derived from a training group was tested in a validation group. RESULTS: A multivariate model created from three gene-expression signatures--termed germinal-center B-cell, stromal-1, and stromal-2 --predicted survival both in patients who received CHOP and patients who received R-CHOP. The prognostically favorable stromal-1 signature reflected extracellular-matrix deposition and histiocytic infiltration. By contrast, the prognostically unfavorable stromal-2 signature reflected tumor blood-vessel density. CONCLUSIONS: Survival after treatment of diffuse large-B-cell lymphoma is influenced by differences in immune cells, fibrosis, and angiogenesis in the tumor microenvironment

DataSHIELD: Taking the analysis to the data, not the data to the analysis

\ua9 The Author 2014; all rights reserved. Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK\u27s proposed \u27care.data\u27 initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property-the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis

Common Variants Show Predicted Polygenic Effects on Height in the Tails of the Distribution, Except in Extremely Short Individuals

Peer reviewe

Molecular Diagnosis of Primary Mediastinal B Cell Lymphoma Identifies a Clinically Favorable Subgroup of Diffuse Large B Cell Lymphoma Related to Hodgkin Lymphoma

Using current diagnostic criteria, primary mediastinal B cell lymphoma (PMBL) cannot be distinguished from other types of diffuse large B cell lymphoma (DLBCL) reliably. We used gene expression profiling to develop a more precise molecular diagnosis of PMBL. PMBL patients were considerably younger than other DLBCL patients, and their lymphomas frequently involved other thoracic structures but not extrathoracic sites typical of other DLBCLs. PMBL patients had a relatively favorable clinical outcome, with a 5-yr survival rate of 64% compared with 46% for other DLBCL patients. Gene expression profiling strongly supported a relationship between PMBL and Hodgkin lymphoma: over one third of the genes that were more highly expressed in PMBL than in other DLBCLs were also characteristically expressed in Hodgkin lymphoma cells. PDL2, which encodes a regulator of T cell activation, was the gene that best discriminated PMBL from other DLBCLs and was also highly expressed in Hodgkin lymphoma cells. The genomic loci for PDL2 and several neighboring genes were amplified in over half of the PMBLs and in Hodgkin lymphoma cell lines. The molecular diagnosis of PMBL should significantly aid in the development of therapies tailored to this clinically and pathogenetically distinctive subgroup of DLBCL

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.Peer reviewe