Concepts of Genomics in Kidney Transplantation

Elucidating the nexus between onco-immunology and kidney transplantation: An insight from precision medicine perspective

The interplay of onco-immunology and kidney transplantation heralds a transformative era in medical science. This integration, while promising, presents significant challenges. Chief among these is the dichotomy of immunosuppression-boosting immunity against malignancies while suppressing it for graft survival. Additionally, limited clinical data on novel therapies, genetic variations influencing responses, economic concerns, and the narrow therapeutic window for post-transplant malignancies necessitate strategic addressal. Conversely, opportunities abound, including personalized immune monitoring, targeted therapies, minimized immunosuppression, and improved patient quality of life. Emphasizing collaborative research and interdisciplinary cooperation, the merging of these fields offers the potential for enhanced graft survival and reduced post-transplant malignancy risks. As we harness modern technology and promote patient-centric care, the vision for the future of kidney transplantation becomes increasingly hopeful, paving the way for more personalized and effective treatments. The article aims to elucidate the critical challenge of balancing immunosuppression to simultaneously combat malignancies and ensure graft survival. It addresses the scarcity of clinical data on novel therapies, the impact of genetic variations on treatment responses, and the economic and therapeutic concerns in managing post-transplant malignancies. Furthermore, it explores the opportunities precision medicine offers, such as personalized immune monitoring, targeted therapies, and reduced immunosuppression, which could significantly improve patient outcomes. Highlighting the importance of collaborative research and interdisciplinary efforts, the article seeks to demonstrate the potential for enhanced graft survival and reduced post-transplant malignancy risks. By leveraging modern technology and prioritizing patient-centric care, it envisions a future where kidney transplantation is more personalized and effective, offering hope for advancements in this field.

Molecular Markers of Kidney Transplantation Outcome: Current Omics Tools and Future Developments

Purpose of review: The emerging field of molecular predictive medicine is aiming to change the traditional medical approach in renal transplantation. Many studies have explored potential biomarker molecules with predictive properties in renal transplantation, issued from omics research. Herein, we review the biomarker molecules of four technologies (i.e., Genomics, Transcriptomics, Proteomics, and Metabolomics) associated with favorable kidney transplant outcomes. Recent findings: Several panels of molecules have been associated with the outcome that the majority of markers are related to inflammation and immune response; although. other molecular ontologies are also represented, such as proteasome, growth, regeneration, and drug metabolism. Throughout this review, we highlight the lack of properly validated statistical demonstration. Indeed, the most preeminent molecular panels either remain at the limited size study stage or are not confirmed during large-scale studies. At the core of this problem, we identify the methodological shortcomings and propose a comprehensive workflow for discovery and validation of molecular biomarkers that aims to improve the relevance of these tools in the future. Summary: Overall, adopting a patient management through omics approach could bring remarkable improvement to transplantation success. An increased effort and investment between scientists, medical biologists, and clinicians seem to be the path toward a proper solution.

Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence

After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical and research challenges. The current burst of studies and other innovative initiatives using artificial intelligence (AI) for a wide range of analytical and practical applications in biomedical areas seems to correlate with the same trend observed in publications in the kidney transplantation field, and points toward the potential of such novel approaches to address the aforementioned aim of improving long-term outcomes of kidney transplant recipients (KTR). However, at the same time, this trend underscores now more than ever the old methodological challenges and potential threats that the research and clinical community needs to be aware of and actively look after with regard to AI-driven evidence. The purpose of this narrative mini-review is to explore challenges for obtaining applicable and adequate kidney transplant data for analyses using AI techniques to develop prediction models, and to propose next steps in the field. We make a call to act toward establishing the strong collaborations needed to bring innovative synergies further augmented by AI, which have the potential to impact the long-term care of KTR. We encourage researchers and clinicians to submit their invaluable research, including original clinical and imaging studies, database studies from registries, meta-analyses, and AI research in the kidney transplantation field.

Genetic Variants Associated With Immunosuppressant Pharmacokinetics and Adverse Effects in the DeKAF Genomics Genome-wide Association Studies

BACKGROUND

The immunosuppressants tacrolimus and mycophenolate are important components to the success of organ transplantation, but are also associated with adverse effects, such as nephrotoxicity, anemia, leukopenia, and new-onset diabetes after transplantation. In this report, we attempted to identify genetic variants which are associated with these adverse outcomes.

METHODS

We performed a genome-wide association study, using a genotyping array tailored specifically for transplantation outcomes containing 722 147 single nucleotide polymorphisms, and 2 cohorts of kidney allograft recipients-a discovery cohort and a confirmation cohort-to identify and then confirm genetic variants associated with immunosuppressant pharmacokinetics and adverse outcomes.

RESULTS

Several genetic variants were found to be associated with tacrolimus trough concentrations. We did not confirm variants associated with the other phenotypes tested although several suggestive variants were identified.

CONCLUSIONS

These results show that adverse effects associated with tacrolimus and mycophenolate are complex, and recipient risk is not determined by a few genetic variants with large effects with but most likely are due to many variants, each with small effect sizes, and clinical factors.