TY - JOUR
T1 - A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation
AU - Ningappa, Mylarappa
AU - Rahman, Syed A.
AU - Higgs, Brandon W.
AU - Ashokkumar, Chethan S.
AU - Sahni, Nidhi
AU - Sindhi, Rakesh
AU - Das, Jishnu
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2022/4/19
Y1 - 2022/4/19
N2 - Selecting the right immunosuppressant to ensure rejection-free outcomes poses unique challenges in pediatric liver transplant (LT) recipients. A molecular predictor can comprehensively address these challenges. Currently, there are no well-validated blood-based biomarkers for pediatric LT recipients before or after LT. Here, we discover and validate separate pre- and post-LT transcriptomic signatures of rejection. Using an integrative machine learning approach, we combine transcriptomics data with the reference high-quality human protein interactome to identify network module signatures, which underlie rejection. Unlike gene signatures, our approach is inherently multivariate and more robust to replication and captures the structure of the underlying network, encapsulating additive effects. We also identify, in an individual-specific manner, signatures that can be targeted by current anti-rejection drugs and other drugs that can be repurposed. Our approach can enable personalized adjustment of drug regimens for the dominant targetable pathways before and after LT in children.
AB - Selecting the right immunosuppressant to ensure rejection-free outcomes poses unique challenges in pediatric liver transplant (LT) recipients. A molecular predictor can comprehensively address these challenges. Currently, there are no well-validated blood-based biomarkers for pediatric LT recipients before or after LT. Here, we discover and validate separate pre- and post-LT transcriptomic signatures of rejection. Using an integrative machine learning approach, we combine transcriptomics data with the reference high-quality human protein interactome to identify network module signatures, which underlie rejection. Unlike gene signatures, our approach is inherently multivariate and more robust to replication and captures the structure of the underlying network, encapsulating additive effects. We also identify, in an individual-specific manner, signatures that can be targeted by current anti-rejection drugs and other drugs that can be repurposed. Our approach can enable personalized adjustment of drug regimens for the dominant targetable pathways before and after LT in children.
KW - liver transplantation
KW - molecular diagnostics
KW - network systems biology
KW - pediatric samples
KW - rejection
KW - systems immunology
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U2 - 10.1016/j.xcrm.2022.100605
DO - 10.1016/j.xcrm.2022.100605
M3 - Article
C2 - 35492246
AN - SCOPUS:85128431068
SN - 2666-3791
VL - 3
JO - Cell Reports Medicine
JF - Cell Reports Medicine
IS - 4
M1 - 100605
ER -