Understanding transplantation outcomes requires an integrated view of immunological, genetic, and clinical determinants that collectively shape graft function over time. Although HLA mismatches are primary drivers of graft injury, emerging evidence highlights the critical contributions of non-HLA antibodies and both T-cell-mediated and antibody-mediated rejections to acute and chronic allograft dysfunction. Advances in molecular immunology and high-resolution next-generation sequencing, including HLA typing, genome-wide variant analysis, and transcriptomic profiling, now enable comprehensive pre- and post-transplant assessment. Donor-derived cell-free DNA and RNA sequencing have further enhanced the ability to monitor graft injury dynamically and noninvasively. The integration of multi-omics data with artificial intelligence and machine learning approaches helps predict immune activation, refine risk stratification, and guide individualized immunosuppressive therapy. Together, these developments represent a transition from descriptive to predictive and personalized transplantation medicine aimed at improving long-term graft survival and patient outcomes following solid organ transplantation.
Shin et al. (Fri,) studied this question.