A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation. A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation

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. Although early acute cellular rejection (ACR) is mediated by cytotoxic T-cells, late rejection also includes antibody-mediated damage in addition to cell-mediated injury. Currently, there are no well-validated blood-based biomarkers for pediatric LT recipients either pre- or post- transplant. Here, we discover and validate separate pre- and post- transplant molecular signatures of LT outcome from whole blood transcriptomes. Using an integrative machine learning approach, we combine transcriptomic data with the high-quality reference human protein interactome network to identify differentially regulated functional sub-components of the network, or “network module signatures”, which drive ACR. Unlike gene signatures, our approach is inherently multivariate, more robust to replication and captures the structure of the underlying molecular network, encapsulating additive effects. We also identify, in a patient-specific manner, network module signatures that can be targeted by current anti-rejection drugs and other mechanisms that can be repurposed. Overall, our approach can enable personalized adjustment of drug regimens for the dominant targetable pathways in pre- and post- LT in children. Overall design: Whole blood samples were obtained from children undergoing liver transplantation at the UPMC-Children’s Hospital of Pittsburgh (CHP)

儿童肝移植（liver transplant, LT）受者中，为实现无排斥反应的移植结局而筛选合适的免疫抑制剂，面临着独特的临床挑战。一款分子预测工具可全面应对此类难题。尽管早期急性细胞性排斥反应（acute cellular rejection, ACR）由细胞毒性T细胞介导，但晚期排斥反应除细胞介导损伤外，还包含抗体介导的组织损伤。目前，针对儿童肝移植受者，无论移植前还是移植后，均尚无经过充分验证的血液源性生物标志物。
本研究从全血转录组（transcriptome）中挖掘并验证了可分别用于肝移植结局预测的移植前与移植后分子特征。本研究采用整合机器学习（integrative machine learning）策略，将转录组数据与高质量的人类参考蛋白质相互作用组网络（protein interactome network）相结合，识别出驱动急性细胞性排斥反应的、该网络中差异调控的功能亚组分，即"网络模块特征"。与基因特征不同，本研究方法本质上为多变量分析，在复现性上更为稳健，且可捕捉底层分子网络的结构特征，同时整合了累加效应。本研究还以患者个体为单位，识别出可被现有抗排斥药物靶向的网络模块特征，以及可进行药物重定位的其他作用机制。总体而言，本方法可针对儿童肝移植前后的主要可靶向通路，实现药物方案的个性化调整。
研究设计：本研究的全血样本采集自匹兹堡大学医学中心（University of Pittsburgh Medical Center, UPMC）-匹兹堡儿童医院（Children’s Hospital of Pittsburgh, CHP）接受肝移植手术的儿童患者。