Concepts of Genomics in Kidney Transplantation

Abstract

Purpose of review Identification of genetic variants to aid in individualized treatment of solid organ allograft recipients would improve graft survival. We will review the current state of knowledge for associations of variants with transplant outcomes. Recent findings Many studies have yet to exhibit robust and reproducible results, however, pharmacogenomic studies focusing on cytochrome P450 (CYP) enzymes, transporters and HLA variants have shown strong associations with outcomes and have relevance towards drugs used in transplant. Genome wide association study data for the immunosuppressant tacrolimus have identified multiple variants in the CYP3A5 gene associated with trough concentrations. Additionally, APOL1 variants had been shown to confer risk to the development of end stage renal disease in African Americans. Summary The field is rapidly evolving and new technology such as next generation sequencing, along with larger cohorts, will soon be commonly applied in transplantation to understand genetic association with outcomes and personalized medicine.

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

“…Recent innovations, like organ-specific genomic profiling and immune checkpoint inhibitors, have shown promise in managing transplantation challenges. By harnessing the power of precision medicine, clinicians can optimize organ matching, predict potential complications, and personalize post-transplant care [ 47 ].…”

“…In the domain of kidney transplantation, precision medicine has the potential to reshape practices, leading to more personalized therapeutic strategies and improved outcomes. In recent years, there's been an exponential growth in understanding the human genome, largely driven by advancements in high-throughput sequencing technologies [ 47 , 48 ]. For kidney transplantation, genomic data has paved the way to assess donor-recipient compatibility at a molecular level beyond traditional HLA-matching [ 49 , 50 ].…”

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

“…Authors investigated a panel of 13 genes: MET , ST5 , and KAAG1 (tumor development or suppression); RNF149, ASB15 , and KLH13 (ubiquitination and proteasome); TGIF1, SPRY4, WNT9A, RXRA , and FJX1 (developmental or growth pathways such as NOTCH/Wnt or RAR ); and CHCHD10 and SERINC5 (energy and membrane repair). They demonstrated good predictive power for the development of fibrosis at 1 year (AUC 0.9) [ 36 ]. Researchers have mobilized to unify genomics databases and predictive model selections, allowing prediction of allograft function, late allograft failure, or tolerance [ 46 ]; with for instance the demonstration of an association with long-term allograft function ( p = 0.004) for polymorphisms of several genes such as CYP3A5 (involved in drug metabolization), CCR5 , FOXP3 , and other genes involved in inflammation and immune response (interleukins, chemokines, TLR pathway, and innate and adaptative immunity mediators); TGF b, VEGF , and other mediators of fibrosis.…”

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

“…This is of concern because it may lead to the exacerbation of health disparities as pointed out by many researchers [20,21], not only unfavorable disparities for specific groups within a country due to underrepresentation in the corresponding database, but also for whole populations belonging to countries that have not started or are lagging behind on the path to performing large data collection projects for Big Data and AI-based research. This, coupled with the increasingly higher impact of genomics in kidney transplantation [22], such as genome-wide association studies, could exclude entire populations from the benefits of advances in long-term post-kidney transplant follow-up and management. Moreover, seemingly powerful models derived from biased databases may have the counterintuitive effect of leading to a false sense of general and robust capacity for prediction and identification of high-risk patients.…”