Abstract
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.