Gene expression signatures identify pediatric patients with multiple organ dysfunction who require advanced life support in the intensive care unit
Authors: Shankar Rama , Leimanis Mara L. , Newbury Patrick A. , Liu Ke , Xing Jing , Nedveck Derek , Kort Eric J. , Prokop Jeremy W , Zhou Guoli , Bachmann André S , Chen Bin , Rajasekaran Surender 
Published: February 2020

Background

Multiple organ dysfunction syndrome (MODS) occurs in the setting of a variety of pathologies including infection and trauma. Some of these patients will further decompensate and require extra corporeal membrane oxygenation (ECMO) as a palliating maneuver to allow time for recovery of cardiopulmonary function. The molecular mechanisms driving progression from MODS to cardiopulmonary collapse remain incompletely understood, and no biomarkers have been defined to identify those MODS patients at highest risk for progression to requiring ECMO support. We hypothesize that molecular features derived from whole blood transcriptomic profiling either alone or in combination with traditional clinical and laboratory markers can prospectively identify these high risk MODS patients in the pediatric intensive care unit (PICU).

Design/Methods

Whole blood RNA-seq profiling was performed for 23 MODS patients at three time points during their ICU stay (at diagnosis of MODS, 72 hours after, and 8 days later), as well as four healthy controls undergoing routine sedation. Of the 23 MODS patients, six required ECMO support (ECMO patients). The predictive power of conventional demographic and clinical features was quantified for differentiating the MODS and ECMO patients. We then compared the performance of markers derived from transcriptomic profiling including (1) transcriptomically imputed leukocyte subtype distribution, (2) relevant published gene signatures and (3) a novel differential gene expression signature computed from our data set. The predictive power of our novel gene expression signature was then validated using independently published datasets.

Results

None of the five demographic characteristics and 14 clinical features, including The Pediatric Logistic Organ Dysfunction (PELOD) score, could predict deterioration of MODS to ECMO at baseline. From previously published sepsis signatures, only the signatures positively associated with patients mortality could differentiate ECMO patients from MODS patients, when applied to our transcriptomic dataset (P-value ranges from 0.01 to 0.04). Deconvolution of bulk RNA-Seq samples suggested that lower neutrophil counts were associated with increased risk of progression from MODS to ECMO (P-value = 0.03, OR=2.82 [95% CI 0.63– 12.45]). A total of 28 genes were differentially expressed between ECMO and MODS patients at baseline (log2 fold change ≥ 1 or ≤ -1 with false discovery rate ≤ 0.2). These genes are involved in protein maintenance and epigenetic-related processes. Further univariate analysis of these 28 genes suggested a signature of six DE genes associated with ECMO (OR > 3.0, P-value ≤ 0.05). Notably, this contains a set of histone marker genes, including H1F0, HIST2H3C, HIST1H2AI, HIST1H4, and HIST1H1B, that were highly expressed in ECMO. A risk score derived from expression of these genes differentiated ECMO and MODS patients in our dataset (AUC = 0.91, 95% CI 0.79-0.1.00, P-value = 7e-04) as well as validation dataset (AUC= 0.73,95% CI 0.53-0.93, P-value = 2e-02).

Conclusions

This study identified lower neutrophils and upregulation of specific histone related genes as a putative signature for deterioration of MODS to ECMO. This study demonstrates that transcriptomic features may be superior to traditional clinical methods of ascertaining severity in patients with MODS.

Author summary

Why was this study done?

Multiple organ dysfunction syndrome (MODS) is a major cause of mortality and morbidity in critically ill pediatric patients who survive the initial physical insult.

A variety of triggers including trauma and infections can lead to MODS in pediatric patients.

The clinical condition of some MODS patients improve while others deteriorate, needing resource-intensive life support such as extracorporeal membrane oxygenation (ECMO).

Mortality is uncommon in PICUs and the need for advanced life support devices, such as ECMO can serve as proxy for mortality.

The decision to initiate ECMO in pediatric patients is often subjective made by a committee of physicians that include surgeons, intensivists and a variety of other subspecialists often in the absence of objective data.

Despite decades of research, no diagnostic criteria or biomarker has been identified that comprehensively assesses severity in MODS patients who may need subsequent ECMO support in the hyperacute phase of injury.

We systematically assessed clinical and transcriptional features as biomarkers for the prediction of the ECMO patients.

What did the researcher do and find?

We investigated various clinical and transcriptional features in 27 patients with MODS at multiple time points (4 CT, 17 MODS, 6 ECMO) at baseline (0h).

We observed that immune response pathways (monocytes, cytokines, NF-kB, and inflammation) were activated in the initiation of MODS, whereas neutrophil level was decreased during deterioration of MODS to ECMO.

A total of 51 DE genes were identified in MODS compared to CT and 28 DE in ECMO compared to MODS at baseline (0h).

We identified the enrichment of immune-related and glycogenolysis processes in MODS compared to CT and enrichment of protein maintenance, DNA repair and epigenetic-related processes in ECMO compared to MODS at baseline (0h).

Logistic regression was used to identify a signature of 6 genes strongly associated with ECMO decision and this signature could help to diagnose MODS patients requiring ECMO.

The transcriptomic signature-based risk scores were further evaluated in an independent cohort.

What do these findings mean?

The compromised level of neutrophils and activation of gene markers including a few histone genes could be used as putative signature for diagnosing the deterioration of MODS to ECMO.

A risk score derived from signature genes could be used to predict the need for ECMO.

This score is superior to traditional clinical criteria and severity scores used in the Pediatric ICU.

The transcriptional signature derived in this study could potentially be used to identify patients in the hyperacute phase of injury that may need higher levels of support like ECMO enabling the selection of an appropriate treatment plan.