Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use.
Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms.
Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77–80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89–97%) accurately predicted endotype status in both ER and ICU validation cohorts.
The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.
Inter-individual clinical variability and lack of predictive and prognostic markers hinders efficient triage and expedient initiation of definitive therapy
Furthermore, treatment delays impact strongly on early and late morbidity/mortality,
while inappropriate antibiotic use has been linked to emergent resistance.
However, these approaches typically lack sensitivity due to heterogeneity arising from individual genetic variation, demographic factors, the infection source and agent, therapeutic intervention, comorbidities including pre-existing immune-suppressive conditions, epigenetics, etc.
Specifically, endotypes can provide more sensitive markers enabling prediction of sepsis severity, risk-stratification, and opportunities for individualized therapies. Previous research has concluded that 2–4 subgroups/endotypes exist in seriously ill sepsis patients in the ICU, that is after sepsis has been confirmed.
Results to date, largely driven by analysis of patient metadata and microarray transcriptomic studies, have indicated a single ICU specific endotype with higher severity scores, but patients with severe sepsis/lethality were scattered across endotypes. Although severe sepsis is no longer recognized as such in the Sepsis-3 definitions, we use it here to discriminate between severely ill patients and those with relatively mild disease. Moreover, the clinical utility of gene expression signatures identified in ICU patients are arguably less useful since patients have already deteriorated
and require intensive care and antibiotics. If we were able to extend these analyses to identify severity markers and/or endotype status within the first hours of ER admission, this would enable more timely, aggressive and/or immunomodulatory interventions to prevent the further progression to more severe sepsis, while sparing broad-spectrum antibiotics when not needed.
Our objective was to identify novel transcriptional diagnostic and risk stratification markers at first presentation to the ER and ICU, when patients show less definitive/non-specific clinical traits, and sepsis diagnosis has not yet been established. We recruited a global cohort of patients from the Netherlands, Colombia, Canada, and Australia and used whole blood RNA-Seq and machine learning to develop gene expression signatures reflecting sepsis severity, cellular reprogramming, and mortality, as well as predicting 5 endotypes differing in overall severity. We exploited protein-protein interaction (PPI) networks to define mechanisms that collectively mediate these groupings. Severity markers and endotypes were further shown to be relevant to an independent prospective cohort of critically ill, tertiary-care ICU patients indicating that early gene expression endotypes were stable and associated with sepsis severity and mortality regardless of progression.
This has the downside of contributing to antimicrobial resistance, since broad-spectrum antibiotics are used even when there is no observable bacterial infection.
In this study, we identified gene expression signatures that can be assessed in patients as early as 1,2 h after ER admission. The signatures can be rapidly measured in hospitals (using PCR, a method that is freely available in most hospital labs) after routine blood collection from prospective sepsis patients). Future studies will be required to enable assessment of these signatures on an appropriate (likely multiplex qRT-PCR) platform before this clinical potential is realized. Assessment of these signatures would provide additional input for physicians to triage patients, in addition to currently used criteria such as lactate, qSOFA, C-reactive protein, Glasgow Coma score and various SIRS assessments.
However, these single biomarkers have showed nominal prognostic accuracy, reflecting their inability to capture a holistic view of the complex immune responses involved in sepsis,
and critically have not been validated in very early sepsis. Thus, they are often correlative rather than predictive. Accordingly, various groups have exploited whole-blood gene expression microarrays to identify, in ICU patients, multi-gene signatures predictive of sepsis; far fewer studies have correlated this with impending deterioration and severity.
The Pediatric PERSEVERE model, identified a biomarker set to estimate the probability of mortality at the time of admission.
In adults, Septicyte Lab
and the FAIM3:PLAC8 ratio
discriminated infected from healthy controls, showing diagnostic accuracy analogous to C-reactive protein,
but discrimination from non-infectious inflammation was less encouraging. All three approaches performed well at identifying survivors, but, unlike the current study, poorly at identifying non-survivors and distinct subgroups varying in morbidity. Intriguingly, the previously elucidated CR severe-sepsis signature as well as DE severity-related and mortality-related gene-sets, could be assembled into cohesive pathways and protein: protein interaction networks, unifying their underlying mechanisms into a single biological program. This fact, together with the mechanistically driven endotype signatures, underpins the potential value of new diagnostics to guide physician decision-making as well as new mechanism-based personalized medicine approaches.
likely because the presence of heterogeneous subgroups was not considered. Identification of reliable endotypes in sepsis, particularly in its earliest stages, enabled us to dissect the heterogeneous molecular responses at play and provided prognostic and severity signatures. Using the largest prospective, observational, and blinded (to patient identity and clinical data) RNA-Seq omics studies performed to date on possible sepsis patients at first clinical presentation, we determined that patients could be assorted into 5 distinct endotypes, four of which were retained in ICU patients, cf. previous studies suggesting only 2 endotypes in the ICU. Our 5 endotypes were characterized by distinct gene expression profiles and signatures, novel mechanistic underpinnings, and different but overlapping clinical factors, including severity. Critically they were identified in an ER discovery group from three countries/continents and confirmed in a fourth independent cohort and in ICU patients. We also predicted our endotypes in patients of the Davenport et al.
(SRS1/2) and Scicluna et al.
(Mars1-4) endotypes (Figure S6). Intriguingly, despite the limitations of using microarray data, the highest lethality groups in the ICU in these studies (SRS1 and Mars1) aligned best with the NPS and INF endotypes respectively, perhaps reflecting differences in selection of patient populations.
Indeed, elevated neutrophil to lymphocyte ratios are associated with poor outcomes in sepsis patients.
Conversely, sepsis-induced neutrophil dysfunction is associated with increased risk of nosocomial and secondary infections, consistent the more severe symptomology and outcomes of the NPS and INF endotypes.
The inflammatory profile of the INF endotype identifies a group of patients who might improve with targeted anti-inflammatory therapies. Interestingly, based on clinical features many studies have uncovered subgroups based on clinical features, termed sub-phenotypes, which appear to display overlap in the symptomatology and outcomes with the NPS and INF endotypes
The IFN, IHD, and ADA endotypes would suggest close monitoring without immediate administration of antibiotics, potentially decreasing the overuse of antibiotics. Intriguingly, severity/mortality signatures, endotypes and underlying mechanisms were clearly conserved between early sepsis in the ER and ICU patients. Taken together, these data reveal that early sepsis signatures are applicable to both a wide variety of ER patients as well as severely ill patients, at the first day of ICU admission.
RH and OP conceived the study. AB, AL, OP, SF and RH contributed to the study design. AB and AL performed bioinformatics analysis. AB and RH drafted the manuscript. AB, AHL, BB, AA, OP, RH contributed to interpretation of data. AB, GCF, and RH contributed to statistical analysis. BT, HB, CdS, CJ, AH, and UT coordinated, and AM, JR, AB, MS, and CJ were directly involved in, sample and patient metadata collection in hospitals. All authors edited the manuscript. RF processed samples for sequencing. AB, OP, AL, BB, AA verified the quality and accuracy of sequencing and clinical data. RH was responsible for obtaining funding and led the study. All authors have read and approved the final version of the manuscript.
Published: January 10, 2022
Received in revised form:
One Sentence Summary: Using machine learning to predict cross-cutting gene expression markers of sepsis severity/lethality, and to triage hospital patients with early sepsis into five endotypes, two of which were strongly associated with severity.
© 2021 The Authors. Published by Elsevier B.V.
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