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.

Keywords: Adaptive immunity; Clinical utility; Host response; Point-of-care; RT-qPCR; Transcriptomic profiling.

Fig. 1

Study flow chart. Patients were enrolled in independent training, verification, and confirmation cohorts. The confirmation cohort was analyzed applying a limited set of 7 transcripts after platform change from microarray to RT-qPCR. BSI: bloodstream infection; SIRS: Systemic Inflammatory Response Syndrome.

Fig. 2

Generation of a genomic score to quantify the host response from the gene expression pattern. A) Heat-map reflecting the gene expression pattern in the verification set: each column reflects a sample of an individual patient with a color code on the top covering the continuum from green (healthy) to magenta (BSI with signs of systemic inflammation); each row reflects an individual transcript. Samples were arranged by GES reflecting the individual host response. Thus, the shift in gene expression depending on disease severity is reflected in the heat-map, with blue indicating low expression and red indicating high expression levels compared to the mean of each individual transcript. B) Scheme of the computation of GES: each triangle is derived from one sample. Its horizontal side is the Euclid distance between the mean healthy pattern and mean pattern in BSI-associated severe sepsis/septic shock; mean healthy value was set to zero and the mean value in BSI with systemic inflammation was set to 100. Its left side corresponds with the distance between the mean healthy pattern and the individual patient pattern, and its right side with the distance between the individual patient pattern and the mean BSI pattern. The GES is computed as the projection of the upper tip of triangle on the base side (for GES the height of the triangle is not relevant). C) Potential of GES (black line) and its components GES UP and GES DOWN (red and blue lines) to predict a given patient's immune state as reflected by a pangenomic assessment of the blood transcriptome. Down-regulated adaptive immune functions (GES DOWN) are impacted particularly by disease severity. In contrast, up-regulated transcripts encoding innate immune functions (GES UP) reached a plateau.

Fig. 3

Individual expression of the selected transcripts of the marker set within the verification cohort. A) A bar chart represents the deviation of each marker from corresponding mean value obtained in healthy controls, consisting of markers reflecting up-regulated effector functions of innate and down-regulated functions of adaptive immunity. Each chart is plotted with the value of the corresponding gene expression score (GES) on the vertical axis and the individual deflection of the 7 transcripts on the horizontal axis for the three individual patients shown in each group, namely those belonging to the 10th, 50th, and 90th percentiles of the corresponding GES values. This presentation reflects the heterogeneity of the response of the various pro- and anti-inflammatory compounds underlying the overall value of the obtained score in individual patients. B) Receiver Operator Characteristics (ROCs) for the score compared with procalcitonin (PCT) and C-Reactive Protein (CRP) to differentiate a state of high grade systemic inflammation from a state of low grade systemic inflammation (AUC_ROC: GES: 0.963 (95% CIs 0.923–1.000); PCT: 0.869 (95% CIs 0.771–0.967); and CRP: 0.935 (95% CIs 0.882–0.988). AUCs GES vs PCT p = 0.020). C) Individual Correlation of the genomic score with PCT and CRP.

Fig. 4

Expression characteristics of the individual transcripts of the genomic score and its sub-scores for patients enrolled in the confirmation cohorts. A) Differences to the mean value of a control cohort expressed as ΔΔCt values (Kenneth and Thomas, 2001) (i.e., deviation from group mean of healthy volunteers) for each of the three up-regulated genes forming the UP score (TLR5: Toll-like receptor 5; CD59: Protectin; CLU: Clusterin); B) ΔΔCt values for each of the four down-regulated genes forming the DOWN score (FGL2: Fibrinogen-like 2; HLA-DPA1: Major histocompatibility complex class II, DP alpha1; CPVL: Carboxypeptidase, vitellogenic-like; IL7R: Interleukin-7 receptor); ΔΔCts represent differences of the time to reaction between patients and healthy controls, both normalized to internal reference genes. C) Values of the calculated genomic master score including both aspects of the host response. Biological functions of the individual transcripts are summarized in Table 1. For all markers and scores, significant differences compared with the “no infection” group were confirmed (**p < 0.01), however no significant differences were observed between possible/probable and definitive infection groups (results of the post-hoc pairwise comparison after Kruskal–Wallis test). D) Receiver Operator Characteristics (ROCs) for the genomic master score to differentiate definite and possible/probable infection from no infection.

Fig. 5

Independence of the developed genomic score from the type of infection and from the implicated pathogens. A) The genomic score is presented stratified according the type of underlying infection/focus of patients enrolled in the two cohorts of the confirmation set, where p-value of one-way-ANOVA between type of infection is 0.749. ABSSTI: acute bacterial skin and soft tissue infection; IAI: intra-abdominal infection; BSI: bloodstream infection; UTI: urinary tract infection; CAP: community-acquired pneumonia; B) the genomic score is presented in relation with the isolated microorganism from patients enrolled in the two cohorts of the confirmation set, where p-value of one-way-ANOVA between the various pathogens is 0.307.

Fig. 6

Early changes of the DOWN-genomic score as an independent prognostic factor. A) Receiver Operator Characteristic curves of the change of the DOWN genomic score and of procalcitonin (PCT) within the first 24 h for the prediction of mortality. Areas under the curve (AUCs) and p-values for the ROC analysis are provided. B) Comparative ROCs of the DOWN score with the presence of infection and with the presence of organ failure to predict mortality. AUCs and p-values for the ROC analysis are provided. C) Survival of patients enrolled in the confirmation cohort from Germany are divided into subgroups with and without early decrease of the down-genomic score by 17%. Those with less than 17% early decrease have prolonged survival. D) The prognostic value of this early decrease of the down-genomic score is confirmed in an independent cohort from Greece. p values of the log-rank tests are provided.

Fig. 7

Time course of GES UP and DOWN, CRP and PCT in survivors and non-survivors of the German cohort. For these patients RNA samples were collected on a daily basis until ICU-discharge or death for a maximum of ten days. Time courses of GES UP and DOWN are presented for survivors and non-survivors, and in comparison to CRP and PCT (each as median and Q25/Q75). The time course of GES DOWN displayed differences primarily for transcripts coding for adaptive immune functions while such differences were observed neither for GES UP nor the single protein biomarkers depending on 100-day mortality.