Elsevier

Human Immunology

Volume 84, Issue 2, February 2023, Pages 89-97
Human Immunology

Review
“Transplantomics” for predicting allograft rejection: real-life applications and new strategies from Network Medicine

https://doi.org/10.1016/j.humimm.2022.11.004Get rights and content

Abstract

Although decades of the reductionist approach achieved great milestones in optimizing the immunosuppression therapy, traditional clinical parameters still fail in predicting both acute and chronic (mainly) rejection events leading to higher rates across all solid organ transplants. To clarify the underlying immune-related cellular and molecular mechanisms, current biomedical research is increasingly focusing on “transplantomics” which relies on a huge quantity of big data deriving from genomics, transcriptomics, epigenomics, proteomics, and metabolomics platforms. The AlloMap (gene expression) and the AlloSure (donor-derived cell-free DNA) tests represent two successful examples of how omics and liquid biopsy can really improve the precision medicine of heart and kidney transplantation. One of the major challenges in translating big data in clinically useful biomarkers is the integration and interpretation of the different layers of omics datasets. Network Medicine offers advanced bioinformatic-molecular strategies which were widely used to integrate large omics datasets and clinical information in end-stage patients to prioritize potential biomarkers and drug targets. The application of network-oriented approaches to clarify the complex nature of graft rejection is still in its infancy. Here, we briefly discuss the real-life clinical applications derived from omics datasets as well as novel opportunities for establishing predictive tests in solid organ transplantation. Also, we provide an original “graft rejection interactome” and propose network-oriented strategies which can be useful to improve precision medicine of solid organ transplantation.

Introduction

One of the major unsolved clinical needs in solid organ transplantation is the ability in predicting those recipients who are at higher risk of developing acute and chronic graft rejection events. This gap arises from the lack of specific biomarkers able to accurately predict and monitor the progression of graft injury or failure at personalized level [1], [2], [3]. At the origin of this challenge there is the paucity of evidence regarding the precise mechanisms which guide the recipient immune responses to destroy the allograft during the early or long-term follow-up [1], [2], [3].

In the last years, the biomedical research community tried to react by harnessing the power of omics platforms and derived big data which allow to measure and analyze large-scale molecular signatures of tissue biopsies as well as circulating cells [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22]. Importantly, rather than studying a single gene, protein, or metabolite, omics strategies permit the exploration of a whole layer of biological information, also at single cell level, which may influence the phenotype and improve medical decision making [1], [2], [3]. While the field of “transplantomics” is relatively young, it can boast the existence of two established predictive tests, such as the AlloMap [23] and the AlloSure assay [24], [25], and other assays which are being in clinical trials, such as the Signatera™ and the Prospera™ tests.

One of the doubts that still pervades the scientific community is how to extrapolate clinically useful messages from heterogeneous omics data and translate them into diagnostic or predictive tests. Network Medicine offers potent strategies to integrate and analyze large omic databases to define pathogenic signatures able to differentiate end-stage patients at individualized level [26], [27], [28], [29]. Surprisingly, the analysis of organ transplantation datasets with the support of network-oriented approaches is still in its infancy.

We outline the real-life clinical applications derived from omics datasets (Table 1) [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22] as well as novel opportunities for establishing additional non-invasive predictive tests in solid organ transplantation (Table 2). In our perspective, we propose that novel network-oriented strategies based on the use of the “graft rejection interactome” may be useful to improve precision medicine of organ transplantation.

Section snippets

Unsolved clinical issues in solid organ transplantation

Despite decades of the reductionist approach, the current strategies to prevent allograft rejection are still inefficacy especially for long-term outcomes. The first step of the standard of care for solid organ transplantation is the accurate risk evaluation in order to identify which end-stage patients would benefit from transplantation and which patients would not [30]. The lack of precise risk scores makes this process even more complicated in the post-transplantation follow-up because the

Real-life applications

“Transplantomics” refers to the study of genomics, epigenomics, transcriptomics, proteomics, and metabolomics, as platforms which are positioned to provide novel potential biomarkers useful to complement the current approaches for an early detection of acute and chronic rejection events as well as additional drug targets [42]. Transcriptomic-based platforms can provide a quantitative assessment of the complete gene expression profile in a specific cellular type or tissue mirroring the molecular

Network Medicine and solid organ transplantation: A novel perspective

The above-discussed omics studies unveiled novel potential biomarkers that correlated with organ rejection and may prognosticate at-risk recipients. Beyond their potential clinical utility, these efforts also provided large available datasets including genomic-, epigenomic-, transcriptomic-, and metabolomic-based profiles for different cell-types and tissues isolated from transplanted patients. The integration and interpretation of the different datasets pose a critical challenge towards the

Conclusions

Despite having many molecular diagnostic tests on the market, there are questions about their clinical utility which is still being assessed in the transplant community. Despite are available the AlloMap test, that can rule out acute cellular rejection in heart transplant recipients, and the ImmuKnow Cylex assay, that can evaluate immunosuppression levels in kidney transplant recipients, these assays have not created the anticipated impact in transplant practice.

Another challenge is that most

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References (70)

  • C. Zhu et al.

    DNA methylation modulates allograft survival and acute rejection after renal transplantation by regulating the mTOR pathway

    Am. J. Transplant.

    (2021)
  • M.R. Costanzo et al.

    The international society of heart and lung transplantation guidelines for the care of heart transplant recipients

    J. Heart Lung Transplant.

    (2010)
  • M. Grskovic et al.

    Validation of a clinical-grade assay to measure donor-derived cell-free dna in solid organ transplant recipients

    J. Mol. Diagn.

    (2016)
  • L. Sommese et al.

    HLA-G and anti-HCV in patients on the waiting list for kidney transplantation

    Adv. Med. Sci.

    (2018)
  • A. Picascia et al.

    From HLA typing to anti-HLA antibody detection and beyond: the road ahead

    Transplant Rev. (Orlando).

    (2016)
  • A. Picascia et al.

    Comprehensive assessment of sensitizing events and anti-HLA antibody development in women awaiting kidney transplantation

    Transpl. Immunol.

    (2016)
  • A. Picascia et al.

    Lights and shadows of anti-HLA antibodies detected by solid-phase assay

    Immunol. Lett.

    (2014)
  • P. Shah et al.

    Transcriptomics in transplantation: more than just biomarkers of allograft rejection

    Am. J. Transplant.

    (2021)
  • M. Mengel et al.

    Banff 2019 Meeting Report: Molecular diagnostics in solid organ transplantation-Consensus for the Banff Human Organ Transplant (B-HOT) gene panel and open source multicenter validation

    Am. J. Transplant.

    (2020)
  • I. Graupera et al.

    Molecular characterization of chronic liver disease dynamics: from liver fibrosis to acute-on-chronic liver failure

    JHEP Rep.

    (2022)
  • A. Alam et al.

    A real-world analysis of the molecular microscope diagnostic system

    Am. J. Transplant.

    (2022)
  • L.D. Snyder et al.

    Highlights from the clinical trials in organ transplantation (CTOT)-20 and CTOT-22 Consortium studies in lung transplant

    Am. J. Transplant.

    (2020)
  • C. Napoli et al.

    Precision medicine in distinct heart failure phenotypes: Focus on clinical epigenetics

    Am Heart J.

    (2020 Jun)
  • A. Picascia et al.

    Epigenetic control of autoimmune diseases: from bench to bedside

    Clin Immunol.

    (2015 Mar)
  • W. Zhang et al.

    Pretransplant transcriptomic signature in peripheral blood predicts early acute rejection

    JCI Insight.

    (2019)
  • A. Verma et al.

    Urinary cell transcriptomics and acute rejection in human kidney allografts

    JCI Insight.

    (2020)
  • F. Kong et al.

    Single-cell transcriptome analysis of chronic antibody-mediated rejection after renal transplantation

    Front Immunol.

    (2022)
  • H. Suryawanshi et al.

    Detection of infiltrating fibroblasts by single-cell transcriptomics in human kidney allografts

    PLoS One.

    (2022)
  • L. Heylen et al.

    Ischemia-induced DNA hypermethylation during kidney transplant predicts chronic allograft injury

    J. Am. Soc. Nephrol.

    (2018)
  • R. Lehmann-Werman et al.

    Monitoring liver damage using hepatocyte-specific methylation markers in cell-free circulating DNA

    JCI Insight.

    (2018)
  • A. Witasp et al.

    Longitudinal genome-wide DNA methylation changes in response to kidney failure replacement therapy

    Sci. Rep.

    (2022)
  • R.M. Rodriguez et al.

    Defining a methylation signature associated with operational tolerance in kidney transplant recipients

    Front Immunol.

    (2021)
  • M.C. Banas et al.

    A Prospective Multicenter Trial to Evaluate Urinary Metabolomics for Non-invasive Detection of Renal Allograft Rejection (PARASOL): study protocol and patient recruitment

    Front Med (Lausanne).

    (2022)
  • A. Loupy et al.

    Gene expression profiling for the identification and classification of antibody-mediated heart rejection

    Circulation

    (2017)
  • M.K. Hsin et al.

    Metabolic profile of ex vivo lung perfusate yields biomarkers for lung transplant outcomes

    Ann. Surg.

    (2018)
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