A Transcriptomic Biomarker to Quantify Systemic Inflammation in Sepsis — A Prospective Multicenter Phase II Diagnostic Study

Abstract

Development of a dysregulated immune response discriminates sepsis from uncomplicated infection. Currently used biomarkers fail to describe simultaneously occurring pro- and anti-inflammatory responses potentially amenable to therapy.Marker candidates were screened by microarray and, after transfer to a platform allowing point-of-care testing, validated in a confirmation set of 246 medical and surgical patients. We identified up-regulated pathways reflecting innate effector mechanisms, while down-regulated pathways related to adaptive lymphocyte functions. A panel of markers composed of three up- (Toll-like receptor 5; Protectin; Clusterin) and 4 down-regulated transcripts (Fibrinogen-like 2; Interleukin-7 receptor; Major histocompatibility complex class II, DP alpha1; Carboxypeptidase, vitellogenic-like) described the magnitude of immune alterations. The created gene expression score was significantly greater in patients with definite as well as with possible/probable infection than with no infection (median (Q25/Q75): 80 (60/101)) and 81 (58/97 vs. 49 (27/66), AUC-ROC = 0.812 (95%-CI 0.755–0.869), p < 0.0001). Down-regulated lymphocyte markers were associated with prognosis with good sensitivity but limited specificity.Quantifying systemic inflammation by assessment of both pro- and anti-inflammatory innate and adaptive immune responses provides a novel option to identify patients-at-risk and may facilitate immune interventions in sepsis.

Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model

“…Without some concept of the overall system behavior space that can inform/approximate the scope of sampling needed to reflect the total set of behaviors possible in the system (which we term the denominator problem of induction ), the danger moving forward is that, for any given specific data set, “some” best fit function can be found for that specific data set, but will intrinsically be limited in it’s applicability to the more general condition. This explains why, in terms of clinical decision support as a path towards precision medicine, physiology-based clinical screening tools [32–34] perform essentially as well as multiplexed biomarker/-omics assays [35–38] for their respective predictive targets (onset of sepsis and sepsis outcome). We assert that the perpetual under-sampling of sepsis behavior space places an upper bound on the predictive capacity of any algorithm based on the under-representative data set (which is a pragmatically/logistically fixed constraint).…”

Drug-induced gene expression profile changes in relation to intestinal toxicity: State-of-the-art and new approaches

“…In addition, traditional drug toxicity biomarkers are often detected when the damage has already been induced whereas gene expression changes may occur instantly, allowing a more efficient prediction of intestinal injury. Despite the promise that gene expression profiling has demonstrated in biomarker investigation [24][25][26][27], and in the understanding of the development of diseases and personalized medicine [28,29], a lack of studies that address drug-induced gene expression responses is still evident.…”

Use of IFNγ/IL10 ratio for stratification of hydrocortisone therapy in patients with septic shock

“…It is increasingly recognized that patients presenting in septic shock are hyper-inflamed yet at the same time immunosuppressed (6)(7)(8). Corticosteroids are traditionally considered to induce immune suppression via the glucocorticoid receptor (GR) and its repressive effect on pro-inflammatory transcription factors such as AP-1 and NFкB (9).…”