Screening for adolescent suicidality in primary care: the bullying-insomnia-tobacco-stress test. A population-based pilot study.

AIM: Adolescents at risk for suicide often see their general practitioner solely for somatic or administrative reasons. A simple screening test given during a conversation would be of substantial help to send a signal and tackle the problem. We propose to update a screening test previously validated in France - the TSTS-Cafard - because of significant changes in the lives of adolescents with the growth of the cyber world since 2000. METHODS: The design and setting was a cross-sectional study involving 912 15-year-old adolescents in 90 French schools. They completed a questionnaire that included the TSTS-Cafard and risk factors extracted from the Health Behaviour in School-Aged Children survey. To improve the test, we selected questions drawn from the recent literature. Answers were analysed according to 'suicidality' = at least one suicide attempt in life or suicidal ideation often over the past 12 months. RESULTS: Suicidality rates were 9.6% for boys and 23.1% for girls. Although the TSTS-Cafard test was generally effective, one question was no longer discriminating. A new test, entitled 'BITS', included only four questions on bullying, insomnia, tobacco and stress, with three levels of response and scores ranging from 0 to 8. Improvement was achieved without loss of performance. Using a cut-off score of 3, we achieved 78% accuracy (area under the curve), 75% sensitivity and 70% specificity. CONCLUSION: The BITS test could allow the question of suicide risk to be addressed during a routine check-up in primary care but the results need to be validated with 13 to 18-year olds.

SegCorr a statistical procedure for the detection of genomic regions of correlated expression.

BACKGROUND: Detecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been successfully used to illustrate the role of mechanisms such as copy number variation (CNV) or epigenetic alterations as factors that may significantly alter expression in large chromosomal regions (gene silencing or gene activation). RESULTS: The identification of correlated regions requires segmenting the gene expression correlation matrix into regions of homogeneously correlated genes and assessing whether the observed local correlation is significantly higher than the background chromosomal correlation. A unified statistical framework is proposed to achieve these two tasks, where optimal segmentation is efficiently performed using dynamic programming algorithm, and detection of highly correlated regions is then achieved using an exact test procedure. We also propose a simple and efficient procedure to correct the expression signal for mechanisms already known to impact expression correlation. The performance and robustness of the proposed procedure, called SegCorr, are evaluated on simulated data. The procedure is illustrated on cancer data, where the signal is corrected for correlations caused by copy number variation. It permitted the detection of regions with high correlations linked to epigenetic marks like DNA methylation. CONCLUSIONS: SegCorr is a novel method that performs correlation matrix segmentation and applies a test procedure in order to detect highly correlated regions in gene expression.

Modeling overdispersion heterogeneity in differential expression analysis using mixtures.

Next-generation sequencing technologies now constitute a method of choice to measure gene expression. Data to analyze are read counts, commonly modeled using negative binomial distributions. A relevant issue associated with this probabilistic framework is the reliable estimation of the overdispersion parameter, reinforced by the limited number of replicates generally observable for each gene. Many strategies have been proposed to estimate this parameter, but when differential analysis is the purpose, they often result in procedures based on plug-in estimates, and we show here that this discrepancy between the estimation framework and the testing framework can lead to uncontrolled type-I errors. Instead, we propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Three consistent statistical tests are developed for differential expression analysis. We show through a wide simulation study that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it reaches the nominal value for the type-I error, while keeping elevate discriminative power between differentially and not differentially expressed genes. The method is finally illustrated on prostate cancer RNA-Seq data.

Genomic instability: a stronger prognostic marker than proliferation for early stage luminal breast carcinomas.

BACKGROUND: The accurate prognosis definition to tailor treatment for early luminal invasive breast carcinoma patients remains challenging. MATERIALS AND METHODS: Two hundred fourteen early luminal breast carcinomas were genotyped with single nucleotide polymorphisms (SNPs) array to determine the number of chromosomal breakpoints as a marker of genomic instability. Proliferation was assessed by KI67 (immunohistochemistry) and genomic grade index (transcriptomic analysis). IHC3 (IHC4 score for HER2 negative tumors) was also determined. RESULTS: In the training set (109 cases), the optimal cut-off was 34 breakpoints with a specificity of 0.94 and a sensitivity of 0.57 (Area under the curve (AUC): 0.81[0.71; 0.91]). In the validation set (105 cases), the outcome of patients with > 34 breakpoints (11 events / 22 patients) was poorer (logrank test p < 0.001; Relative Risk (RR): 3.7 [1.73; 7.92]), than that of patients with < 34 breakpoints (19 events / 83 patients).Whereas genomic grade and KI67 had a significant prognostic value in univariate analysis in contrast to IHC3 that failed to have a statistical significant prognostic value in this series, the number of breakpoints remained the only significant parameter predictive of outcome (RR: 3.47, Confidence Interval (CI [1.29; 9.31], p = 0.014)) in multivariate analysis . CONCLUSION: Genomic instability, defined herein as a high number of chromosomal breakpoints, in early stage luminal breast carcinoma is a stronger prognostic marker than proliferation.

ExactDAS: an exact test procedure for the detection of differential alternative splicing in microarray experiments.

The aim of this paper is to propose a test procedure for the detection of differential alternative splicing across conditions for tiling array or exon chip data. While developed in a mixed model framework, the test procedure is exact (avoiding computational burden) and applicable to a large variety of contrasts, including several previously published ones. A simulation study is presented to evaluate the robustness and performance of the method. It is found to have a good detection power of genes under differential alternative splicing, even for five biological replicates and four probes per exon. The methodology also enables the comparison of various experimental designs through exact power curves. This is illustrated with the comparison of paired and unpaired experiments. The test procedure was applied to two publicly available cancer data sets based on exon arrays, and showed promising results.

Transcriptomic analysis of the dialogue between Pseudorabies virus and porcine epithelial cells during infection.

BACKGROUND: Transcriptomic approaches are relevant for studying virus-host cell dialogues to better understand the physiopathology of infection and the immune response at the cellular level. Pseudorabies virus (PrV), a porcine Alphaherpesvirus, is a good model for such studies in pig. Since PrV displays a strong tropism for mucous epithelial cells, we developed a kinetics study of PrV infection in the porcine PK15 epithelial cell line. To identify as completely as possible, viral and cellular genes regulated during infection, we simultaneously analyzed PrV and cellular transcriptome modifications using two microarrays i.e. a laboratory-made combined SLA/PrV microarray, consisting of probes for all PrV genes and for porcine genes contained in the Swine Leukocyte Antigen (SLA) complex, and the porcine generic Qiagen-NRSP8 oligonucleotide microarray. We confirmed the differential expression of a selected set of genes by qRT-PCR and flow cytometry. RESULTS: An increase in the number of differentially expressed cellular genes and PrV genes especially from 4 h post-infection (pi) was observed concomitantly with the onset of viral progeny while no early global cellular shutoff was recorded. Many cellular genes were down-regulated from 4 h pi and their number increased until 12 h pi. UL41 transcripts encoding the virion host shutoff protein were first detected as differentially expressed at 8 h pi. The viral gene UL49.5 encoding a TAP inhibitor protein was differentially expressed as soon as 2 h pi, indicating that viral evasion via TAP inhibition may start earlier than the cellular gene shutoff. We found that many biological processes are altered during PrV infection. Indeed, several genes involved in the SLA class I antigenic presentation pathway (SLA-Ia, TAP1, TAP2, PSMB8 and PSMB9), were down-regulated, thus contributing to viral immune escape from this pathway and other genes involved in apoptosis, nucleic acid metabolism, cytoskeleton signaling as well as interferon-mediated antiviral response were also modulated during PrV infection. CONCLUSION: Our results show that the gene expression of both PrV and porcine cells can be analyzed simultaneously with microarrays, providing a chronology of PrV gene transcription, which has never been described before, and a global picture of transcription with a direct temporal link between viral and host gene expression.