Prediction of Liver Transplant Rejection With a Biologically Relevant Gene Expression Signature

Personalized Nanomedicine-Mediated immune regulation for Anti-Rejection in organ transplantation

The advent of personalized medicine and nanomedicine has led to significant advancements in organ transplantation. Personalized medicine leverages individual patient profiles, including genetic, epigenetic, and immune characteristics, to tailor treatment regimens. Nanomedicine, involving the use of nanoparticles and nanotechnology, offers precise drug delivery and innovative diagnostic tools. The integration of personalized nanomedicine into these fields has the potential to revolutionize transplantation by enhancing graft survival, minimizing adverse effects, and achieving immune tolerance. This review explores the current landscape of personalized nanomedicine for organ transplantation, focusing on immune modulation and therapeutic strategies tailored to individual patient profiles. We also discuss future research directions, including large-scale clinical trials, and regulatory considerations. This review concludes by examining the potential of personalized nanomedicine in improving long-term transplant outcomes and enhancing patient quality of life.

Prediction model of the T cell-mediated rejection after liver transplantation in children and adults: A case-controlled study

OBJECTIVE

T cell-mediated rejection (TCMR) is a major concern following liver transplantation (LT), and identifying its predictors could help improve post-transplant prognosis. This study aimed to develop a model to predict the risk of TCMR in children and adults after LT.

METHOD

Pre-transplant demographic characteristics, intraoperative parameters, and especially early post-transplant laboratory data for 1221 LT recipients (1096 adults and 125 children) were obtained from the Hospital, University, between 1 January 2015, and 1 January 2022. These data were analyzed to develop the prediction model.

RESULT

The incidence of TCMR was higher in pediatric LT recipients than in adults (17.6% vs. 6.4%, P < 0.001). In adult recipients, seven predictors were identified: donor sex, recipient age, recipient height, and post-transplant levels of serum direct bilirubin, urea, platelets, and neutrophil-to-lymphocyte ratio. In pediatric recipients, four predictors were identified: post-transplant levels of serum monocyte percentage, direct bilirubin, albumin, and gamma-glutamyl transferase. The area under the model's curve incorporating these variables for predicting TCMR after LT was 0.713 (95% confidence interval, CI: 0.655-0.770) in adults and 0.786 (95% CI: 0.675-0.896) in children. Decision curve analyses demonstrated the clinical significance of the model.

CONCLUSION

This study developed a prediction model that may be useful in identifying high-TCMR-risk populations in both adult and pediatric LT recipients.

miRNAs, dd-cf-DNA, and Chemokines as Potential Noninvasive Biomarkers for the Assessment of Clinical Graft Evolution and Personalized Immunosuppression Requirement in Solid Organ Transplantation

ABSTRACT

The use of noninvasive biomarkers may reduce the need for biopsy and guide immunosuppression adjustments during transplantation. The scientific community in solid organ transplantation currently considers that chemokines, T- and B-cell immunophenotypes, and gene expression, among other molecular biomarkers, have great potential as diagnostic and predictive biomarkers for graft evolution; however, in clinical practice, few valid early biomarkers have emerged. This review focuses on the most relevant scientific advances in this field in the last 5 years regarding the role of 3 biomarkers: miRNAs, chemokines, and ddcf-DNA, in both adult and pediatric populations. An update was provided on the scores based on the combination of these biomarkers. The most-featured articles were identified through a literature search of the PubMed database. This review provides a comprehensive analysis of the potential clinical applications of these biomarkers in the diagnosis and prediction of graft outcomes and discusses the reasons why none have been implemented in clinical practice to date. Translating these biomarkers into routine clinical practice and combining them with pharmacogenetics and pharmacokinetic monitoring is challenging; however, it is the key to present/future individualized immunosuppressive therapies. It is essential that they be shown to be applicable and robust in real-life patient conditions and properly evaluate their added value when combined with the standard-of-care factor monitoring for graft clinical assessment. Partnership strategies among scientists, academic institutions, consortia, including expert working groups and scientific societies, and pharmaceutical and/or biotechnology companies should promote the development of prospective, randomized, multicenter intervention studies for adequate clinical validation of these biomarkers and their monitoring frequency, and their commercialization to make them available to transplant physicians.

Plasma immune signatures can predict rejection-free survival in the first year after pediatric liver transplantation

BACKGROUND & AIMS

After pediatric liver transplantation (pLT), children undergo life-long immunosuppression since reliable biomarkers for the assessment of rejection probability are scarce. In the multicenter (n = 7) prospective clinical cohort "ChilSFree" study, we aimed to characterize longitudinal dynamics of soluble and cellular immune mediators during the first year after pLT and identify early biomarkers associated with outcome.

METHODS

Using a Luminex-based multiplex technique paired with flow cytometry, we characterized longitudinal dynamics of soluble immune mediators (SIMs, n = 50) and immune cells in the blood of 244 patients at eight visits over 1 year: before, and 7/14/21/28 days and 3/6/12 months after pLT.

RESULTS

The unsupervised clustering of patients based on SIM profiles revealed six unique SIM signatures associated with clinical outcome. From three signatures linked to improved outcome, one was associated with 1-year-long rejection-free survival and stable graft function and was characterized by low levels of pro-inflammatory SIMs (CXCL8/9/10/12, CCL7, SCGF-β, sICAM-1), and high levels of regenerative (SCF, TNF-β) and pro-apoptotic (TRAIL) SIMs (all, p <0.001, fold change >100). Of note, this SIM signature appeared 2 weeks after pLT and remained stable over the entire year, pointing towards its potential as a novel early biomarker for minimizing or weaning immunosuppression. In the blood of these patients, a higher frequency of CD56bright natural killer cells (p <0.01), a known hallmark also associated with operationally tolerant pLT patients, was detected. The concordance of the model for prediction of rejection based on identified SIM signatures was 0.715, and 0.795, in combination with living-related transplantation as a covariate, respectively.

CONCLUSIONS

SIM blood signatures may enable the non-invasive and early assessment of rejection risks in the first year after pLT, paving the way for improved clinical management.

IMPACT AND IMPLICATIONS

ChilSFree represents the largest pediatric liver transplant (pLT) cohort with paired longitudinal data on soluble immune mediators (SIMs) and immune phenotyping in the first year after pLT. SIM signatures allow for the selection of rejection-free patients 2 weeks after pLT independently of patient diagnosis, sex, or age. The SIM signatures may enable the non-invasive and early assessment of rejection risks, paving the way for minimization or withdrawal of immunosuppression after pLT.

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model's efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Predictive model containing gene signature and shear wave elastography to predict patient outcomes after Kasai surgery in biliary atresia

AIMS

Infants with biliary atresia (BA) are treated with Kasai portoenterostomy (KPE) surgery, but many BA patients need subsequent salvage liver transplants. The aim of this study is to develop a comprehensive gene-clinical model based on two-dimensional shear wave elastography (2DSWE), liver gene expression, and other clinical parameters to predict response to KPE for BA patients.

METHODS

Differentially expressed gene patterns between liver samples of BA (n = 102) and non-BA control (n = 14) were identified using RNA sequencing analysis. Biliary atresia patients were then randomly assigned to training and validation cohorts. Gene classifier based on the differentially expressed genes was built in the training cohort. Nomogram models with and without gene classifier were further constructed and validated for predicting native liver survival of BA patients. The utility of the nomograms was compared by C-index.

RESULTS

Using the least absolute shrinkage and selection operator model, we generated a nine-gene prognostic classifier. The nomogram based on the nine-gene classifier, age, preoperative 2DSWE, and albumin had the better C-index compared to gene classifier alone in the training cohort (0.83 [0.76-0.90] vs. 0.69 [0.61-0.77], p = 0.003) and the validation cohort (0.74 [0.67-0.82] vs. 0.62 [0.55-0.70], p = 0.001). Using risk scores developed from the nomogram, the 12-month survival rates of BA patients with native liver were 35.7% (95% confidence interval [CI], 22.7-56.3) in the high-risk group and 80.8% (95% CI, 63.4-100.0) in the low-risk group in the validation cohort.

CONCLUSIONS

The comprehensive genetic-clinical nomogram based on preoperative 2DSWE, liver gene expression, and other clinical parameters can accurately predict response to KPE.

How to Estimate the Probability of Tolerance Long-Term in Liver Transplant Recipients

BACKGROUND

Operational tolerance as the ability to accept the liver transplant without pharmacological immunosuppression is a common phenomenon in the long-term course. However, it is currently underutilized due to a lack of simple diagnostic support and fear of rejection despite its recognized benefits. In the present work, we present a simple score based on clinical parameters to estimate the probability of tolerance.

PATIENTS AND METHODS

In order to estimate the probability of tolerance, clinical parameters from 82 patients after LT who underwent weaning from the IS for various reasons at our transplant center were extracted from a prospectively organized database and analyzed retrospectively. Univariate testing as well as multivariable logistic regression analysis were performed to assess the association of clinical variables with tolerance in the real-world setting.

RESULTS

The most important factors associated with tolerance after multivariable logistic regression were IS monotherapy, male sex, history of hepatocellular carcinoma pretransplant, time since LT, and lack of rejection. These five predictors were retained in an approximate model that could be presented as a simple scoring system to estimate the clinical probability of tolerance or IS dispensability with good predictive performance (AUC = 0.89).

CONCLUSION

In parallel with the existence of a tremendous need for further research on tolerance mechanisms, the presented score, after validation in a larger collective preferably in a multicenter setting, could be easily and safely applied in the real world and already now address all three levels of prevention in LT patients over the long-term course.

Novel Noninvasive Biomarkers in Liver Transplantation: A Tool on the Doorstep of Clinical Utilization

Biomarkers have the potential to transform the detection, treatment, and outcomes of liver transplant complications, though their application is limited because of the lack of prospective validation. Although many genetic, proteomic, and immune markers correlating with allograft rejection and graft dysfunction have been described, evaluation of these markers in combination and validation among a broad liver transplant recipient population remain understudied. In this review, we present evidence supporting biomarker applications in 5 clinical liver transplant scenarios: (i) diagnosis of allograft rejection, (ii) prediction of allograft rejection, (iii) minimization of immunosuppression, (iv) detection of fibrosis and recurrent disease, and (v) prediction of renal recovery following liver transplantation. Current limitations for biomarker utilization and opportunities for further investigation are discussed. Accurate risk assessment, diagnosis, and evaluation of treatment responses using such noninvasive tools will pave the way for a more personalized and precise approach to management of the liver transplant patients that has profound potential to reduce morbidity and improve graft and patient longevity.

A plasmatic score using a miRNA signature and CXCL-10 for accurate prediction and diagnosis of liver allograft rejection

Introduction

The use of noninvasive biomarkers may avoid the need for liver biopsy (LB) and could guide immunosuppression adjustment in liver transplantation (LT). The aims of this study were: to confirm the predictive and diagnostic capacity of plasmatic expression of miR-155-5p, miR-181a-5p, miR-122-5p and CXCL-10 for assessing T-cell mediated rejection (TCMR) risk; to develop a score based on a panel of noninvasive biomarkers to predict graft rejection risk and to validate this score in a separate cohort.

Methods

A prospective, observational study was conducted with a cohort of 79 patients followed during the first year after LT. Plasma samples were collected at predetermined time points for the analysis of miRNAs and the CXCL-10. Patients with LFTs abnormalities were submitted to a LB to rule out rejection, assessing previous and concurrent expression of the biomarkers to evaluate their predictive and diagnostic ability. Information from 86 patients included in a previous study was collected and used as a validation cohort.

Results

Twenty-four rejection episodes were diagnosed in 22 patients. Plasmatic CXCL-10 concentration and the expression of the three miRNAs were significantly elevated prior to and at the moment of the diagnosis of rejection. We developed a logistic model for rejection prediction and diagnosis, which included CXCL-10, miR-155-5p and miR-181a-5p. The area under the ROC curve (AUROC) for rejection prediction was 0.975 (79.6% sensitivity, 99.1% specificity, 90,7% PPV; 97.7% NPV; 97.1% correctly classified) and 0.99 for diagnosis (87.5% sensitivity, 99.5% specificity, 91.3% PPV; 99.3% NPV; 98.9% correctly classified). In the validation cohort (n=86; 14 rejections), the same cut-off points were used obtaining AUROCs for rejection prediction and diagnosis of 0.89 and 0.92 respectively. In patients with graft dysfunction in both cohorts the score could discriminate those with rejection regarding other causes with an AUROC of 0.98 (97.3% sensitivity, 94.1%specificity).

Conclusion

These results suggest that the clinical implementation of the monitoring of this noninvasive plasmatic score may allow the prediction and diagnosis of rejection and identify patients with graft dysfunction due to rejection, helping with a more efficient guide for immunosuppressive therapy adjustment. This finding warrants the development of prospective biomarker-guided clinical trials.

Noninvasive biomarkers for the diagnosis and management of autoimmune hepatitis

Autoimmune hepatitis (AIH) is a rare disease of unclear etiology characterized by loss of self-tolerance that can lead to liver injury, cirrhosis, and acute liver failure. First-line treatment consists of systemic corticosteroids, or budesonide, and azathioprine, to which most patients are initially responsive, although predictors of response are lacking. Relapses are very common, correlate with histological activity despite normal serum transaminases, and increase hepatic fibrosis. Furthermore, current regimens lead to adverse effects and reduced quality of life, whereas medication titration is imprecise. Biomarkers that can predict the clinical course of disease, identify patients at elevated risk for relapse, and improve monitoring and medication dosing beyond current practice would have high clinical value. Herein, we review novel candidate biomarkers in adult and pediatric AIH based on prespecified criteria, including gene expression profiles, proteins, metabolites, and immune cell phenotypes in different stages of AIH. We also discuss biomarkers relevant to AIH from other immune diseases. We conclude with proposed future directions in which biomarker implementation into clinical practice could lead to advances in personalized therapeutic management of AIH.

Expression of unfolded protein response genes in post-transplantation liver biopsies

Background

Cholestatic liver diseases are a major source of morbidity and mortality that can progress to end-stage liver disease and hyperbilirubinemia is a hallmark of cholestasis. There are few effective medical therapies for primary biliary cholangitis, primary sclerosing cholangitis and other cholestatic liver diseases, in part, due to our incomplete understanding of the pathogenesis of cholestatic liver injury. The hepatic unfolded protein response (UPR) is an adaptive cellular response to endoplasmic reticulum stress that is important in the pathogenesis of many liver diseases and recent animal studies have demonstrated the importance of the UPR in the pathogenesis of cholestatic liver injury. However, the role of the UPR in human cholestatic liver diseases is largely unknown.

Methods

RNA was extracted from liver biopsies from patients after liver transplantation. RNA-seq was performed to determine the transcriptional profile and hepatic UPR gene expression that is associated with liver injury and cholestasis.

Results

Transcriptome analysis revealed that patients with hyperbilirubinemia had enhanced expression of hepatic UPR pathways. Alternatively, liver biopsy samples from patients with acute rejection had enhanced gene expression of LAG3 and CDK1. Pearson correlation analysis of serum alanine aminotransferase, aspartate aminotransferase and total bilirubin levels demonstrated significant correlations with the hepatic expression of several UPR genes, as well as genes involved in hepatic bile acid metabolism and inflammation. In contrast, serum alkaline phosphatase levels were correlated with the level of hepatic bile acid metabolism gene expression but not liver UPR gene expression.

Conclusions

Overall, these data indicate that hepatic UPR pathways are increased in cholestatic human liver biopsy samples and supports an important role of the UPR in the mechanism of human cholestatic liver injury.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02459-8.

Donor-derived cell-free DNA levels predict graft injury in liver transplant recipients

Donor-derived cell-free DNA (dd-cfDNA) has been evaluated as a rejection marker in organ transplantation. This study sought to assess the utility of dd-cfDNA to diagnose graft injury in liver transplant recipients (LTR) and as a predictive biomarker prior to different causes of graft dysfunction. Plasma from single and multicenter LTR cohorts was analyzed for dd-cfDNA. Phenotypes of treated biopsy-proven acute rejection (AR, N = 57), normal function (TX, N = 94), and acute dysfunction no rejection (ADNR; N = 68) were divided into training and test sets. In the training set, dd-cfDNA was significantly different between AR versus TX (AUC 0.95, 5.3% cutoff) and AR versus ADNR (AUC 0.71, 20.4% cutoff). Using these cutoffs in the test set, the accuracy and NPV were 87% and 100% (AR vs. TX) and 66.7% and 87.8% (AR vs. ADNR). Blood samples collected serially from LTR demonstrated incremental elevations in dd-cfDNA prior to the onset of graft dysfunction (AR > ADNR), but not in TX. Dd-cfDNA also decreased following treatment of rejection. In conclusion, the serial elevation of dd-cfDNA identifies pre-clinical graft injury in the context of normal liver function tests and is greatest in rejection. This biomarker may help detect early signs of graft injury and rejection to inform LTR management strategies.

The Blood Proteoform Atlas: A reference map of proteoforms in human hematopoietic cells

Human biology is tightly linked to proteins, yet most measurements do not precisely determine alternatively spliced sequences or posttranslational modifications. Here, we present the primary structures of ~30,000 unique proteoforms, nearly 10 times more than in previous studies, expressed from 1690 human genes across 21 cell types and plasma from human blood and bone marrow. The results, compiled in the Blood Proteoform Atlas (BPA), indicate that proteoforms better describe protein-level biology and are more specific indicators of differentiation than their corresponding proteins, which are more broadly expressed across cell types. We demonstrate the potential for clinical application, by interrogating the BPA in the context of liver transplantation and identifying cell and proteoform signatures that distinguish normal graft function from acute rejection and other causes of graft dysfunction.