Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure

Abstract

Absence of early outcome biomarkers for Pediatric Acute Liver Failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome sub-groups. Serum samples from 101 participants in the PALF study, collected over the first 7 days following enrollment, were assayed for 27 inflammatory mediators. Outcomes (Spontaneous survivors [S, n=61], Non-survivors [NS, n=12], and liver transplant patients [LTx, n=28]) were assessed at 21 days post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all patient sub-groups. The networks in S and LTx were similar, and differed from NS. Dynamic Network Analysis suggested similar dynamic connectivity in S and LTx, but a more highly-interconnected network in NS that increased with time. A Dynamic Robustness Index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient sub-groups. Our results suggest that increasing inflammatory network connectivity is associated with non-survival in PALF, and may ultimately lead to better patient outcome stratification.

Keywords: biomarker; computational analysis; inflammation; liver disease; networks.

Figure 1.

Dynamic Bayesian Network (DBN) analysis of circulating inflammatory mediators in PALF patients. Circulating inflammatory mediators in serum samples from PALF spontaneous survivors (S, n = 61 patients), nonsurvivors (NS, n = 12 patients) and liver transplant recipients (LTx, n = 28 patients) were measured and DBN analysis was performed, as described in Materials and Methods. Inflammatory mediators are shown as nodes, and the arrows connecting them suggest an influence of one mediator on the one(s) it is connected to. The arrows do not distinguish positive from negative influences. Semicircular arrows suggest either positive or negative feedback of a given mediator on itself.

Figure 2.

Dynamic network analysis (DyNA) of circulating inflammatory mediators in PALF patients. Circulating inflammatory mediators in serum samples from PALF spontaneous survivors (S, n = 61 patients), nonsurvivors (NS, n = 12 patients) and liver transplant recipients (LTx, n = 28 patients) were measured and DyNA (stringency level = 0.95) was performed during seven time frames, d 0–1, d 1–2, d 2–3, d 3–4, d 4–5, d 5–6, d 6–7, as described in Materials and Methods. Panel A shows an overview of all the networks and mediator connections (closed circles represent mediators with at least one connection to another mediator; open circles represent mediators that had no connections to other mediators as determined by DyNA). Panel B represents the total number of connections for each group of patients over all time intervals. Panel C shows the network complexity for each group of patients during each of the seven time frames calculated, as described in Materials and Methods.

Figure 3.

Significant circulating inflammatory mediators and connections in PALF patients (DyNA, high stringency). Circulating inflammatory mediators in serum samples from PALF patients were measured and DyNA (stringency level = 0.95) was performed during seven time frames, d 0–1, d 1–2, d 2–3, d 3–4, d 4–5, d 5–6, d 6–7, as described in Materials and Methods. Results show the significant mediators based on dynamic patterns and network connectivity in three main groups of PALF patients (panel A: nonsurvivors, n = 12; panel B: LTx recipients, n = 28; and panel C: spontaneous survivors, n = 61).

Figure 4.

Dynamic robustness index in PALF. Circulating inflammatory mediators in serum samples from PALF patients were measured. DyNA (stringency levels = 0.7 and 0.95) was performed during seven time frames, d 0–1, d 1–2, d 2–3, d 3–4, d 4–5, d 5–6, d 6–7, and the dynamic robustness index for each patient group (survivors [n = 61 patients], nonsurvivors [n = 12 patients], LTx recipients [n = 28 patients]) was calculated, as described in Materials and Methods.

Figure 5.

Dynamic network analysis (DyNA) of circulating inflammatory mediators in PALF patients (age-matched survivors versus nonsurvivors). Circulating inflammatory mediators in serum samples from PALF spontaneous survivors (S, n = 45 patients) that closely matched nonsurvivors (NS, n = 12 patients) were measured and DyNA (stringency level = 0.95) was performed, as described in Materials and Methods. The figure shows the network complexity during each of the seven time frames calculated, as described in Materials and Methods, and the total number of connections for each group of patients over all time intervals (inset).