Patient stratification using plasma cytokines and their regulators in sepsis: relationship to outcomes, treatment effect and leucocyte transcriptomic subphenotypes

Abstract

Rationale: Heterogeneity of the host response within sepsis, acute respiratory distress syndrome (ARDS) and more widely critical illness, limits discovery and targeting of immunomodulatory therapies. Clustering approaches using clinical and circulating biomarkers have defined hyper-inflammatory and hypo-inflammatory subphenotypes in ARDS associated with differential treatment response. It is unknown if similar subphenotypes exist in sepsis populations where leucocyte transcriptomic-defined subphenotypes have been reported.

Objectives: We investigated whether inflammatory clusters based on cytokine protein abundance were seen in sepsis, and the relationships with previously described transcriptomic subphenotypes.

Methods: Hierarchical cluster and latent class analysis were applied to an observational study (UK Genomic Advances in Sepsis (GAinS)) (n=124 patients) and two clinical trial datasets (VANISH, n=155 and LeoPARDS, n=484) in which the plasma protein abundance of 65, 21, 11 circulating cytokines, cytokine receptors and regulators were quantified. Clinical features, outcomes, response to trial treatments and assignment to transcriptomic subphenotypes were compared between inflammatory clusters.

Measurements and main results: We identified two (UK GAinS, VANISH) or three (LeoPARDS) inflammatory clusters. A group with high levels of pro-inflammatory and anti-inflammatory cytokines was seen that was associated with worse organ dysfunction and survival. No interaction between inflammatory clusters and trial treatment response was found. We found variable overlap of inflammatory clusters and leucocyte transcriptomic subphenotypes.

Conclusions: These findings demonstrate that differences in response at the level of cytokine biology show clustering related to severity, but not treatment response, and may provide complementary information to transcriptomic sepsis subphenotypes.

Trial registration number: ISRCTN20769191, ISRCTN12776039.

Keywords: bacterial infection; critical care; respiratory infection.

Figure 1

Diagram demonstrating the analyses that were performed across the three datasets and the comparisons that were made. Each column represents a dataset with the red boxes demonstrating the analysis that was performed, with the columns they span reflecting the cohorts the analysis was performed on. GAinS, Genomic Advances in Sepsis.

Figure 2

Unsupervised patient structure in Genomic Advances in Sepsis (GAinS) (n=124, left), VANISH (n=155, middle) and LeoPARDS (n=484, right). (A) Heatmaps and cluster dendrograms from hierarchical clustering, patients shown as columns and plasma cytokines and related proteins as rows. Clustering was performed on natural logarithm transformed plasma protein concentrations but for clarity of presentation heatmaps are displayed as z-scaled values. Solid bars represent sepsis response signature (SRS) assignments in GAinS and VANISH (light purple=SRS1, dark purple=SRS2, blank=no assignment available). A full-size version of the heatmap in GAinS where all plasma proteins are labelled and heatmaps from all datasets where non-scaled concentrations are plotted are available as online supplemental figure S2. (B) Principal component analysis scores plot where data points are shaded based on median concentration of all plasma cytokines and related proteins measured in each sample, and the symbol shapes represent the cluster assignments (circles=low cytokine cluster, squares=intermediate cytokine cluster, triangles=high cytokine cluster, centroids for each group are shown with the relevant symbol in black). Percentage of variance explained by the principal components (PC) 1/2 are stated in parentheses. (C) Volcano plots comparing high cytokine clusters with low cytokine clusters (GAinS and VANISH), where the false discovery rate (FDR) is obtained from a Mann-Whitney U test with a Benjamini-Hochberg correction, or high versus low, high versus intermediate and intermediate versus low clusters (LeoPARDS), where FDR is obtained from a post hoc, pairwise Wilcoxon test with Benjamini-Hochberg correction for both the number of pairwise comparisons and the number of plasma proteins compared.

Figure 3

Estimated latent class analysis class means of each indicator by class for LeoPARDS (panel A) and VANISH (panel B) trials. Indicators have been standardised to ensure zero population mean and facilitate comparison. Indicators are ordered by the difference between classes for almost all indicators. In the LeoPARDS trial, indicators are ordered by the difference between class 1 and class 3, the estimated means for class 2 were in between those for classes 1 and 3. IL, interleukin; MPO, myeloperoxidase; P/F, PaO₂:FiO₂; sICAM, soluble intercellular adhesion molecule.

Figure 4

Gene expression comparisons between hierarchical cluster analysis inflammatory clusters, and correlation between inflammatory cluster comparisons and sepsis response signatures (SRS) transcriptomic subphenotype comparisons. (A) Volcano plot for low versus high cytokine clusters in VANISH and (B) the enriched Gene Ontology Biological Processes of the differentially expressed genes. (C) Log₂ fold change correlations between the plasma cytokine cluster comparisons in VANISH and in Genomic Advances in Sepsis (GAinS). (D, E) Log₂ fold change correlations of the inflammatory cluster comparisons and the SRS comparisons in GAinS (D) and in VANISH (E). P values are shown for tests of correlations using Pearson’s product moment correlation. In the volcano plot (A), red points indicate probes (n=667) for 559 differentially expressed genes (false discovery rate (FDR) <0.05 and fold change >1.5, dashed lines indicate these thresholds). Genes on the right-hand side have higher expression in the low cytokine cluster.