Project description:Physiological, anatomical, and clinical laboratory analytic scoring systems (APACHE, Injury Severity Score (ISS)) have been utilized, with limited success, to predict outcome following injury. We hypothesized that a peripheral blood leukocyte gene expression score could predict outcome, including multiple organ failure, following severe blunt trauma. Contributor: The Inflammation and the Host Response to Injury Large Scale Collaborative Research Program Keywords: expression profiles cRNA derived from whole blood leukocytes obtained within 12 hours of hospital admission provided gene expression data for the entire genome that were used to create a gene expression score for each patient. Expression profiles from healthy volunteers were averaged to create a reference gene expression profile which was used to compute a difference from reference (DFR) score for each patient. This score described the overall genomic response of patients within the first 12 hours following severe blunt trauma. Regression models were used to compare the association of the DFR, APACHE and ISS scores with outcome.
Project description:Chronic lymphocytic leukemia (CLL) is a common and heterogeneous disease. An accurate prediction of outcome is highly relevant for the development of personalized treatment strategies. Microarray technology was shown to be a useful tool for the development of prognostic gene expression scores. However, there are no gene expression scores which are able to predict overall survival in CLL based on the expression of few genes that are better than established prognostic markers. We correlated 151 CLL microarray data sets with overall survival using Cox regression and supervised principal component analysis to derive a prognostic score. This score based on the expression levels of eight genes and was validated in an independent group of 149 CLL patients by quantitative real time PCR. The score was predictive for overall survival and time to treatment in univariate Cox regression in the validation data set (both: p<0.001) and in a multivariate analysis after adjustment for 17p and 11q deletions and the IgVH-status. The score achieved superior prognostic accuracy compared to models based on genomic aberrations and IgVH-status and may support personalized therapy. Analysis of 151 samples of peripheral blood mononuclear cells (107 HGU-133plus2; 44 HGU-133A; 44 HGU-133B) from adult patients with chronic lymphocytic leukemia (CLL)
Project description:Physiological, anatomical, and clinical laboratory analytic scoring systems (APACHE, Injury Severity Score (ISS)) have been utilized, with limited success, to predict outcome following injury. We hypothesized that a peripheral blood leukocyte gene expression score could predict outcome, including multiple organ failure, following severe blunt trauma. Contributor: The Inflammation and the Host Response to Injury Large Scale Collaborative Research Program Keywords: expression profiles
Project description:Chronic lymphocytic leukemia (CLL) is a common and heterogeneous disease. An accurate prediction of outcome is highly relevant for the development of personalized treatment strategies. Microarray technology was shown to be a useful tool for the development of prognostic gene expression scores. However, there are no gene expression scores which are able to predict overall survival in CLL based on the expression of few genes that are better than established prognostic markers. We correlated 151 CLL microarray data sets with overall survival using Cox regression and supervised principal component analysis to derive a prognostic score. This score based on the expression levels of eight genes and was validated in an independent group of 149 CLL patients by quantitative real time PCR. The score was predictive for overall survival and time to treatment in univariate Cox regression in the validation data set (both: p<0.001) and in a multivariate analysis after adjustment for 17p and 11q deletions and the IgVH-status. The score achieved superior prognostic accuracy compared to models based on genomic aberrations and IgVH-status and may support personalized therapy.
Project description:The study was aimed to identify mechanisms linked to complicated courses after severe trauma by a systems biology approach. In severe trauma, overwhelming systemic inflammation can result in adverse events and the development of complications, including sepsis. In a prospective study, RNA samples from circulating leukocytes from patients with multiple injury (injury severity score ⥠17) were analyzed for dynamic changes in gene expression over a period of 21 days by whole genome screening. Based on their clinical presentation, patients were divided into two subgroups: patients with secondary sepsis after trauma (n=5) and patients with systemic inflammation without infection (n=5). Expression cluster were defined by correlating gene expression data with clinical outcome parameters. Using unsupervised clustering, patients with systemic inflammation only and patients with sepsis showed a distinct expression pattern and the discrimination of clinical presentation was reflected by clustering of the samples. Explorative gene set analysis revealed robust upregulation of genes related to âhemoglobin metabolism/oxygen transportâ and âpathogenic E.coli infectionâ. 10 patients with multi-system trauma (ISS ⥠17 points) admitted to the Division of Trauma Surgery at the University Hospital Zurich were included. Whole blood from trauma patients was collected within the first 6 h after trauma (day 0) and on days 1, 2, 3, 5, 7, 10, 14, and 21. Total cellular RNA from circulating leukocytes was isolated using PaxGene Blood RNA Kit (PreAnalytix) for transcriptome profiling. RNA from blood of trauma patients was extracted and subjected to microarray analysis for comparison of longitudinal transcriptomic responses of patients. RNA samples of circulating leukocytes covering time points directly after admission (D0) and on the consecutive days (D1-D21) were subject to multifactorial microarray data analysis: Differences in dynamics of transcripts were assessed by contrasting time- and individual-resolved changes for sepsis and systemic inflammation without infection.