Are there hidden biological types of septic shock in children?
Clustering children with septic shock by the genes active in their blood revealed three subgroups, and the one with switched-off immune and zinc-related genes was the sickest and most likely to die.
Source
Identification of pediatric septic shock subclasses based on genome-wide expression profiling
Study at a glance
- Design
- Other — Observational discovery study: whole-blood microarray profiles taken within 24 hours of PICU admission were clustered without using outcome data (hierarchical clustering, then ANOVA-filtered genes, principal components and K-means), and the resulting subclasses were compared on clinical features; leave-one-out cross-validation tested how well a reduced gene set reassigned patients.
- N
- N=98 · 98 children with septic shock from 11 institutions, compared against 32 healthy control children for normalisation; 67 patients and all controls had appeared in earlier analyses of the same dataset.
- Population
- Children aged 10 years or younger admitted to paediatric intensive care units in the United States with septic shock.
- Outcome
- Gene-expression-defined subclasses and their association with illness severity (PRISM III), organ failure and mortality.
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What they did
The team took blood samples from 98 children in septic shock within a day of intensive care admission and measured the activity of tens of thousands of genes using microarrays. Without telling the analysis who survived, they clustered patients by gene-expression pattern, checked the clusters with principal component analysis, and then compared the resulting groups on illness severity, organ failure and death. They also tested whether a smaller set of pathway genes could reassign patients to the right group.
What they found
Three subclasses (A, B and C) emerged, separated by thousands of differently expressed genes. Subclass A had higher illness-severity scores, more organ failure and higher mortality (36%) than B and C, and its patients showed broad repression of genes for adaptive immunity, glucocorticoid-receptor signalling and zinc biology, without differing in lymphocyte counts. A support vector machine using 307 pathway genes reassigned 89 of 98 patients (91%) to their subclass in leave-one-out cross-validation.
The limits
What it doesn't show
This is a discovery analysis in only 98 patients with no independent validation cohort, so the three subclasses might not replicate; the cross-validation reused the same data that defined the groups. The clustering choices (fold-change filters, where to cut the tree) were somewhat arbitrary, and whole-blood RNA could partly reflect different mixes of white cells rather than changes within cells. The study shows associations with outcome, not that repressed immune or zinc genes cause death or that targeting them would help.
Key terms
- Septic shock
- A life-threatening state in which infection triggers a body-wide response with dangerously low blood pressure and poor organ perfusion.
- Unsupervised clustering
- Grouping patients purely by similarity in their data, without using outcomes or labels, to discover natural subgroups.
- Gene expression profiling
- Measuring the activity (RNA level) of thousands of genes at once, here using microarrays on whole-blood samples.
- Adaptive immunity
- The antigen-specific arm of the immune system run by T and B lymphocytes; its suppression in sepsis is sometimes called immune paralysis.
- Leave-one-out cross-validation
- Repeatedly training a classifier on all but one patient and testing on the one left out, to estimate how well it would classify new cases.
- Endotype
- A subtype of a condition defined by a distinct underlying biological mechanism rather than by symptoms alone.
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Quiz yourself
How were the septic shock subclasses first identified?
Common questions
Why does it matter that septic shock has subclasses?
Many immune-modulating drugs for sepsis have failed in trials, possibly because they were given to a mixed population; if patients differ biologically, therapies could be targeted to the subgroup most likely to benefit.
Could subclass A just be younger or have fewer lymphocytes?
Subclass A children were younger than subclass B but not subclass C, and lymphocyte counts did not differ across groups, so the authors argue the expression pattern is not simply an artefact of age or cell counts, though white-cell mix remains possible.
Can doctors use this test at the bedside?
Not from this study. It is a first discovery analysis using microarrays; the subclasses would need replication in new patients and a fast, simpler assay before clinical use.
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