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