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Biomarker Validation on the ‘Analysis of geNe Expression and bioMarkers fOr poiNt-of-care dEcision support in Sepsis’ (ANEMONES) study

ABSTRACT: Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. Here, we describe identification of transcriptional mRNA biomarkers able to identify severe systemic inflammation and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study. All patients were recruited in Intensive Care Units (ICUs) from multiple UK hospitals including 59 patients with abdominal sepsis, 84 patients with pulmonary sepsis, 42 SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), at four time points including 30 healthy control donors. Multiple clinical parameters were measured, including SOFA score etc., with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using PBL mRNA microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools. Nineteen select high-performance, differentially-expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed ‘indicators of inflammation’ (IoI), including CD177, FAM20A and OLAH. Combinations of these were trialed. Best-performing minimal panels e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (ROC/AUC>0.99). Twenty select entities were differentially-expressed between sepsis and SIRS (FC>2.0, p-value<0.05), termed ‘SIRS or Sepsis’ (SoS) biomarkers. Panels of biomarkers able to differentiate sepsis from SIRS were also identified and performance assessed using AUCROC. The best performing panel was CMTM5/CETP/PLA2G7/MIA/MPP3 using our dataset (AUCROC=0.9758). The IoI and SoS signatures were evaluated on other independent gene expression datasets, with some reduced performance observed, which maybe in part due to study/platform technical variation.

ORGANISM(S): Homo sapiens

PROVIDER: GSE236713 | GEO | 2024/01/08

REPOSITORIES: GEO

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Project description:Goal of the experiment: To identify correlated genes, pathways and groups of patients with systemic inflammatory response syndrome and septic shock that is indicative of biologically important processes active in these patients. Background: We measured gene expression levels and profiles of children with systemic inflammatory response syndrome (SIRS) and septic shock as a means for discovering patient sub-groups and gene signatures that are active in disease-affected individuals and potentially in patients with poor outcomes. Methods: Microarray and bioinformatics analyses of 123 microarray chips representing whole blood derived RNA from controls, children with SIRS, and children with septic shock. Results: A discovery-based filtering approach was undertaken to identify genes whose expression levels were altered in patients with SIRS or septic shock. Clustering of these genes identified 3 Major and several minor sub-groups of patients with SIRS or septic shock. The three groups differed with respect to incidence of septic shock and trended toward differences in mortality. Statistical analyses demonstrated that 6,435 gene probes were differentially regulated between the three patient sub-groups (false discovery rate < 0.001%). Of these gene probes, 623 gene probes within 7 major gene ontologies accounted for the majority of group differentiation. Network analyses of these 623 gene probes demonstrated 5 major gene networks that were differentially expressed between the 3 groups. Statistical comparison of septic shock survivors and non-survivors identified one major gene network that was under expressed in a high fraction of the non-survivors and identified potential biomarkers for poor outcome. Conclusions: This is the first genome-level demonstration of pediatric patient sub-groups with SIRS and septic shock. The sub-groups differ clinically and differentially express 5 major gene networks. We have identified gene signatures and potential biomarkers associated with poor outcome in children with septic shock. These data represent a major advancement in our genome-level understanding of pediatric SIRS and septic shock. Experiment Overall Design: Children < 10 years of age admitted to the pediatric intensive care unit and meeting the criteria for either SIRS or septic shock were eligible for the study. SIRS and septic shock were defined based on pediatric-specific criteria. We did not use separate categories of "sepsis" or "severe sepsis". Patients meeting criteria for "sepsis" or "severe sepsis" were placed in the categories of SIRS and septic shock, respectively, for study purposes. Control patients were recruited from the outpatient or inpatient departments of the participating institutions using the following exclusion criteria: a recent febrile illness (within 2 weeks), recent use of anti-inflammatory medications (within 2 weeks), or any history of chronic or acute disease associated with inflammation. Experiment Overall Design: After obtaining informed consent, blood samples were obtained on Day 1 of the study, and when possible on Day 3 of the study. Blood samples were divided for RNA extraction and isolation of serum. Severity of illness was calculated based on the PRISM III score. Organ failure was defined based on pediatric-specific criteria. Annotated clinical and laboratory data were collected daily while in the intensive care unit. Study patients were placed in the study categories of SIRS or Septic Shock on Day 1 of the study. On Day 3 of the study, patients were classified as SIRS, Septic Shock, or SIRS resolved (no longer meeting criteria for SIRS). All study patients were followed for 28 days to determine mortality or survival. Clinical, laboratory, and biological data were entered and stored using a web-based data base developed locally.

Project description:Expression data from CD8+ T cells and CD68+ monocytes from patients with hemophagocytic lymphohistiocytosis, sepsis, and persistent systemic inflammatory response syndrome Hemophagocytic lymphohistiocytosis (HLH) is a syndrome characterized by pathologic immune activation in which prompt recognition and initiation of immune suppression is essential for survival. Children with HLH have many overlapping clinical features with critically ill children with sepsis and persistent systemic inflammatory response syndrome (SIRS) in whom alternative therapies are indicated. To determine if plasma biomarkers could differentiate HLH from other inflammatory conditions and to better define a ‘core inflammatory signature’ of HLH, concentrations of inflammatory plasma proteins were compared in 40 patients with HLH to 47 pediatric patients with severe sepsis or SIRS. Seventeen of 135 analytes were significantly different in HLH plasma compared to SIRS/sepsis, including increased interferon-gamma (IFNg)-regulated chemokines CXCL9, CXCL10 and CXCL11. Further, a 5-analyte plasma protein classifier including these chemokines was able to differentiate HLH from SIRS/sepsis. Gene expression in CD8+ T cells and CD68+ monocytes from blood were also enriched for IFNg pathway signatures in peripheral blood cells from patients with HLH compared to SIRS/sepsis. This study identifies differential expression of inflammatory proteins as a diagnostic strategy to identify critically ill children with HLH. Further, comprehensive unbiased analysis of inflammatory plasma proteins and global gene expression demonstrates that IFNg signaling is uniquely elevated in HLH. In addition to demonstrating the ability of diagnostic criteria for HLH, sepsis and SIRS to identify groups with distinct inflammatory patterns, results from this study support the potential for prospective evaluation of inflammatory biomarkers to aid in diagnosis of and optimizing therapeutic strategies for children with distinctive hyperinflammatory syndromes.

Project description:Introduction: Sepsis is a complex immunological response to infection characterized by early hyperinflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on SIRS differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. Methods: This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. Results: Based on preliminary microarray analyses comparing HC and sepsis groups. A panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. AUC ROC curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 - 92%. Conclusions: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. GEO Note: Data made available represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20). This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. The GEO data represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).

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Biomarker Validation on the ‘Analysis of geNe Expression and bioMarkers fOr poiNt-of-care dEcision support in Sepsis’ (ANEMONES) study

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