The incidence and severity of viral complications after cellular therapy are highly variable. Recent publications describe relevant interactions between the human Cytomegalovirus (CMV) and host immunity in recipients of allogeneic hematopoietic cell transplantation (HCT). Although immune monitoring is routinely performed in HCT patients, validated cut-off levels correlating with transplant outcomes such as survival or CMV reactivation are mostly limited to day +100, which is later than the median time for CMV reactivation in the absence of medical prophylaxis. To address this gap in early risk assessment, we applied an unsupervised machine learning technique based on clustering of day +30 CD4+ helper T cell count data, and identified relevant cut-off levels within the diverse spectrum of early CD4+ reconstitution. These clusters were stratified for CMV recipient serostatus to identify early risk groups that predict clinical HCT outcome. Indeed, the new risk groups predicted subsequent clinical events such as NRM, OS, and high CMV peak titers better than the most established predictor, i.e., the positive CMV recipient serostatus (R+). More specifically, patients from the R+/low CD4+ subgroup strongly associated with high CMV peak titers and increased 3-year NRM (subdistribution hazard ratio (SHR) 10.1, 95% CI 1.38–73.8, p = 0.023), while patients from the R-/very high CD4+ subgroup showed comparable NRM risks (SHR 9.57, 95% CI 1.12–81.9, p = 0.039) without such an association. In short, our study established novel cut-off levels for early CD4+ T cells via unsupervised learning and supports the integration of host cellular immunity into clinical risk-assessment after HCT in the context of CMV reactivation.
A comprehensive knowledge of human leukocyte antigen (HLA) molecular variation worldwide is essential in human population genetics research and disease association studies and is also indispensable for clinical applications such as allogeneic hematopoietic cell transplantation, where ensuring HLA compatibility between donors and recipients is paramount. Enormous progress has been made in this field thanks to several decades of HLA population studies allowing the development of helpful databases and bioinformatics tools. However, it is still difficult to appraise the global HLA population diversity in a synthetic way. We thus introduce here a novel approach, based on approximately 2000 data sets, to assess this complexity by providing a fundamental synopsis of the most frequent HLA alleles observed in different regions of the world. This new knowledge will be useful not only as a fundamental reference for basic research, but also as an efficient guide for clinicians working in the field of transplantation.