Improving social function with real-world social-cognitive remediation in schizophrenia: Results from the RemedRugby quasi-experimental trial.

BACKGROUND: Functional capacity (FC) has been identified as a key outcome to improve real-world functioning in schizophrenia. FC is influenced by cognitive impairments, negative symptoms, self-stigma and reduced physical activity (PA). Psychosocial interventions targeting FC are still under-developed. METHODS: we conducted a quasi-experimental study evaluating the effects of an exercise-enriched integrated social cognitive remediation (SCR) intervention (RemedRugby [RR]) compared with an active control group practicing Touch Rugby (TR). To our knowledge, this is the first trial to date evaluating the effectiveness of such a program provided in a real-life environment. RESULTS: Eighty-seven people with schizophrenia were included and allocated to either the RR group (n = 57) or the TR group (n = 30) according to the routine clinical practice of the recruiting center. Outcomes were evaluated at baseline and post-treatment in both groups and after 6 months of follow-up in the RR group using standardized scales for symptom severity, social functioning, self-stigma, and a large cognitive battery. After treatment we observed moderate to large improvements in social function (Personal and Social Performance Scale [PSP], p < 0.001, d = 1.255), symptom severity (Positive and Negative Syndrome Scale [PANSS] negative, p < 0.001, d = 0.827; PANSS GP, p < 0.001, d = 0.991; PANSS positive, p = 0.009, d = 0.594), verbal abstraction (p = 0.008, d = 0.554), aggression bias (p = 0.008, d = 0.627), and self-stigma (stereotype endorsement, p = 0.019, d = 0.495; discrimination experiences, p = 0.047; d = 0.389) that were specific to the RR group and were not observed in participants playing only TR. Effects were persistent over time and even larger between post-treatment and follow-up. CONCLUSIONS: Exercise-enriched integrated SCR appears promising to improve real-life functioning in schizophrenia. Future research should investigate the potential effects of this intervention on neuroplasticity and physical fitness.

Correction: Unbiased Estimation of Mutation Rates under Fluctuating Final Counts.

[This corrects the article DOI: 10.1371/journal.pone.0101434.].

Large-scale microarray profiling reveals four stages of immune escape in non-Hodgkin lymphomas.

Non-Hodgkin B-cell lymphoma (B-NHL) are aggressive lymphoid malignancies that develop in patients due to oncogenic activation, chemo-resistance, and immune evasion. Tumor biopsies show that B-NHL frequently uses several immune escape strategies, which has hindered the development of checkpoint blockade immunotherapies in these diseases. To gain a better understanding of B-NHL immune editing, we hypothesized that the transcriptional hallmarks of immune escape associated with these diseases could be identified from the meta-analysis of large series of microarrays from B-NHL biopsies. Thus, 1446 transcriptome microarrays from seven types of B-NHL were downloaded and assembled from 33 public Gene Expression Omnibus (GEO) datasets, and a method for scoring the transcriptional hallmarks in single samples was developed. This approach was validated by matching scores to phenotypic hallmarks of B-NHL such as proliferation, signaling, metabolic activity, and leucocyte infiltration. Through this method, we observed a significant enrichment of 33 immune escape genes in most diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) samples, with fewer in mantle cell lymphoma (MCL) and marginal zone lymphoma (MZL) samples. Comparing these gene expression patterns with overall survival data evidenced four stages of cancer immune editing in B-NHL: non-immunogenic tumors (stage 1), immunogenic tumors without immune escape (stage 2), immunogenic tumors with immune escape (stage 3), and fully immuno-edited tumors (stage 4). This model complements the standard international prognostic indices for B-NHL and proposes that immune escape stages 3 and 4 (76% of the FL and DLBCL samples in this data set) identify patients relevant for checkpoint blockade immunotherapies.

Several immune escape patterns in non-Hodgkin's lymphomas.

Follicular Lymphomas (FL) and diffuse large B cell lymphomas (DLBCL) must evolve some immune escape strategy to develop from lymphoid organs, but their immune evasion pathways remain poorly characterized. We investigated this issue by transcriptome data mining and immunohistochemistry (IHC) of FL and DLBCL lymphoma biopsies. A set of genes involved in cancer immune-evasion pathways (Immune Escape Gene Set, IEGS) was defined and the distribution of the expression levels of these genes was compared in FL, DLBCL and normal B cell transcriptomes downloaded from the GEO database. The whole IEGS was significantly upregulated in all the lymphoma samples but not in B cells or other control tissues, as shown by the overexpression of the PD-1, PD-L1, PD-L2 and LAG3 genes. Tissue microarray immunostainings for PD-1, PD-L1, PD-L2 and LAG3 proteins on additional biopsies from 27 FL and 27 DLBCL patients confirmed the expression of these proteins. The immune infiltrates were more abundant in FL than DLBCL samples, and the microenvironment of FL comprised higher rates of PD-1+ lymphocytes. Further, DLBCL tumor cells comprised a higher proportion of PD-1+, PD-L1+, PD-L2+ and LAG3+ lymphoma cells than the FL tumor cells, confirming that DLBCL mount immune escape strategies distinct from FL. In addition, some cases of DLBCL had tumor cells co-expressing both PD-1, PD-L1 and PD-L2. Among the DLBCLs, the activated B cell (ABC) subtype comprised more PD-L1+ and PD-L2+ lymphoma cells than the GC subtype. Thus, we infer that FL and DLBCL evolved several pathways of immune escape.

Weighted Kolmogorov Smirnov testing: an alternative for Gene Set Enrichment Analysis.

Gene Set Enrichment Analysis (GSEA) is a basic tool for genomic data treatment. Its test statistic is based on a cumulated weight function, and its distribution under the null hypothesis is evaluated by Monte-Carlo simulation. Here, it is proposed to subtract to the cumulated weight function its asymptotic expectation, then scale it. Under the null hypothesis, the convergence in distribution of the new test statistic is proved, using the theory of empirical processes. The limiting distribution needs to be computed only once, and can then be used for many different gene sets. This results in large savings in computing time. The test defined in this way has been called Weighted Kolmogorov Smirnov (WKS) test. Using expression data from the GEO repository, tested against the MSig Database C2, a comparison between the classical GSEA test and the new procedure has been conducted. Our conclusion is that, beyond its mathematical and algorithmic advantages, the WKS test could be more informative in many cases, than the classical GSEA test.

A six gene expression signature defines aggressive subtypes and predicts outcome in childhood and adult acute lymphoblastic leukemia.

Abnormal gene expression in cancer represents an under-explored source of cancer markers and therapeutic targets. In order to identify gene expression signatures associated with survival in acute lymphoblastic leukemia (ALL), a strategy was designed to search for aberrant gene activity, which consists of applying several filters to transcriptomic datasets from two pediatric ALL studies. Six genes whose expression in leukemic blasts was associated with prognosis were identified:three genes predicting poor prognosis (AK022211, FASTKD1 and STARD4) and three genes associated with a favorable outcome (CAMSAP1, PCGF6 and SH3RF3). Combining the expression of these 6 genes could successfully predict prognosis not only in the two discovery pediatric ALL studies, but also in two independent validation cohorts of adult patients, one from a publicly available study and one consisting of 62 newly recruited Chinese patients. Moreover, our data demonstrate that our six gene based test is particularly efficient in stratifying MLL or BCR.ABL negative patients. Finally, common biological traits characterizing aggressive forms of ALL in both children and adults were found, including features of dormant hematopoietic stem cells, suggesting new therapeutic strategies.

Unbiased estimation of mutation rates under fluctuating final counts.

Estimation methods for mutation rates (or probabilities) in Luria-Delbrück fluctuation analysis usually assume that the final number of cells remains constant from one culture to another. We show that this leads to systematically underestimate the mutation rate. Two levels of information on final numbers are considered: either the coefficient of variation has been independently estimated, or the final number of cells in each culture is known. In both cases, unbiased estimation methods are proposed. Their statistical properties are assessed both theoretically and through Monte-Carlo simulation. As an application, the data from two well known fluctuation analysis studies on Mycobacterium tuberculosis are reexamined.

Curbing false discovery rates in interpretation of genome-wide expression profiles.

Fisher's exact test is widely used in biomedical research, particularly in genomic profile analysis. Since in most databases, the frequency distribution of genes is right skewed, we show here that its use can lead to excessive false-positive discoveries. We propose to apply Zelen's exact test on a stratification of the gene set; this solves the false discovery problem, and should avoid misleading interpretations of lists of genes produced by various genome-wide analysis technologies.

Fluctuation analysis: can estimates be trusted?

The estimation of mutation rates and relative fitnesses in fluctuation analysis is based on the unrealistic hypothesis that the single-cell times to division are exponentially distributed. Using the classical Luria-Delbrück distribution outside its modelling hypotheses induces an important bias on the estimation of the relative fitness. The model is extended here to any division time distribution. Mutant counts follow a generalization of the Luria-Delbrück distribution, which depends on the mean number of mutations, the relative fitness of normal cells compared to mutants, and the division time distribution of mutant cells. Empirical probability generating function techniques yield precise estimates both of the mean number of mutations and the relative fitness of normal cells compared to mutants. In the case where no information is available on the division time distribution, it is shown that the estimation procedure using constant division times yields more reliable results. Numerical results both on observed and simulated data are reported.

The gene expression profile of phosphoantigen-specific human γδ T lymphocytes is a blend of αß T-cell and NK-cell signatures.

Global transcriptional technologies have revolutionised the study of lymphoid cell populations, but human γδ T lymphocytes specific for phosphoantigens remain far less deeply characterised by these methods despite the great therapeutic potential of these cells. Here we analyse the transcriptome of circulating TCRVγ(+) γδ T cells isolated from healthy individuals, and their relation with those from other lymphoid cell subsets. We report that the gene signature of phosphoantigen-specific TCRVγ(+) γδ T cells is a hybrid of those from αß T and NK cells, with more 'NK-cell' genes than αß T cells have and more 'T-cell' genes than NK cells. The expression profile of TCRVγ(+) γδ T cells stimulated with phosphoantigen recapitulates their immediate physiological functions: Th1 cytokine, chemokine and cytotoxic activities reflect their high mitotic activity at later time points and do not indicate antigen-presenting functions. Finally, such hallmarks make the transcriptome of γδ T cells, whether resting or clonally expanding, clearly distinctive from that of NK/T or peripheral T-cell lymphomas of the γδ subtype.