TY - JOUR
T1 - Data-driven modeling for precision medicine in pediatric acute liver failure
AU - Zamora, Ruben
AU - Vodovotz, Yoram
AU - Mi, Qi
AU - Barclay, Derek
AU - Yin, Jinling
AU - Horslen, Simon
AU - Rudnick, David
AU - Loomes, Kathleen M.
AU - Squires, Robert H.
N1 - Funding Information:
This work was supported by National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases grant UO1 DK072146 (A Multi-Center Group to Study Acute Liver Failure in Children). We would like to thank Regina M Hardison, Barbara Pavliakova Walters, Sharon M Lawlor and Steven H Belle from the Epidemiology Data Center (University of Pittsburgh) for their invaluable help. This work was supported by A Multi-Center Group to Study Acute Liver Failure in Children and the collaborative efforts of the following current and former principal and coinvestigators of the Pediatric Acute Liver Failure Study (by site): University of Pittsburgh: Robert H Squires, MD, and Benjamin L Shneider, MD; Cincinnati Children′s Hospital: John Bucuvalas, MD, and Mike Leonis, MD, PhD; Lurie Children′s Hospital of Chicago: Estella Alonso, MD; University of Texas Southwestern (Dallas): Norberto Rodriguez-Baez, MD; Seattle Children′s Hospital: Karen Murray, MD, and Simon Horslen, MB, ChB; Children′s Hospital Colorado (Aurora): Michael R Narkewicz, MD; St Louis Children′s Hospital: David Rudnick, MD, PhD, and Ross W Shepherd, MD; University of California at San Francisco: Philip Rosenthal, MD; Hospital for Sick Children (Toronto, Canada): Vicky Ng, MD; Riley Hospital for Children (Indianapolis, IN): Girish Subbarao, MD; Emory University (Atlanta, GA): Rene Romero, MD; Children′s Hospital of Philadelphia: Elizabeth Rand, MD, and Kathy Loomes, MD; Kings College–London (England): Anil Dhawan, MD; Birmingham Children′s Hospital (England): Dominic Dell Olio, MD, and Deirdre A Kelly, MD Texas Children′s Hospital (Houston): Saul Karpen MD PhD; Mt. Sinai Medical Center (Miami Beach, FL): Nanda Kerkar, MD; Univer sity of Michigan (Ann Arbor): M James Lopez, MD, PhD; Children′s Hospital Medical Center (Boston): Scott Elisofon, MD, and Maureen Jonas, MD; Johns Hopkins University (Baltimore, MD): Kathleen Schwarz, MD; Columbia University (New York, NY): Steven Lobritto, MD.
Publisher Copyright:
© 2016, Uninversity of Michigan. All rights reserved.
PY - 2016
Y1 - 2016
N2 - The 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 subgroups. Serum samples from 101 participants in the PALF study, collected over the first 7 d following enrollment, were assayed for 27 inflammatory mediators. Outcomes (spontaneous survivors [S, n = 61], nonsurvivors [NS, n = 12] and liver transplant patients [LTx, n = 28]) were assessed at 21 d 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 subgroups. 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 subgroups. Our results suggest that increasing inflammatory network connectivity is associated with nonsurvival in PALF and could ultimately lead to better patient outcome stratification.
AB - The 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 subgroups. Serum samples from 101 participants in the PALF study, collected over the first 7 d following enrollment, were assayed for 27 inflammatory mediators. Outcomes (spontaneous survivors [S, n = 61], nonsurvivors [NS, n = 12] and liver transplant patients [LTx, n = 28]) were assessed at 21 d 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 subgroups. 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 subgroups. Our results suggest that increasing inflammatory network connectivity is associated with nonsurvival in PALF and could ultimately lead to better patient outcome stratification.
UR - http://www.scopus.com/inward/record.url?scp=85010403136&partnerID=8YFLogxK
U2 - 10.2119/molmed.2016.00183
DO - 10.2119/molmed.2016.00183
M3 - Article
C2 - 27900388
AN - SCOPUS:85010403136
SN - 1076-1551
VL - 22
SP - 821
EP - 829
JO - Molecular Medicine
JF - Molecular Medicine
ER -