Graft versus host disease (GVHD) is a common complication and the leading cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Pharmacological immunosuppression used in GVHD prophylaxis and treatment lacks specificity and can increase the likelihood of infection and relapse. Regulatory T lymphocytes (Tregs) play a vital role in restraining excessive immune responses and inducing peripheral immune tolerance. In particular, clinical trials have demonstrated that Tregs can prevent and treat GVHD, without increasing the risk of relapse and infection. Hence, adoptive transfer of Tregs to control GVHD using their immunosuppressive properties represents a promising therapeutic approach. To optimally apply Tregs for control of GVHD, a thorough understanding of their biology is necessary. In this review, we describe the biological characteristics of Tregs, including how the stability of FOXP3 expression can be maintained. We will also discuss the mechanisms underlying Tregs-mediated modulation of GVHD and approaches to effectively increase Tregs’ numbers. Finally, we will examine the developing trends in the use of Tregs for clinical therapy.
Forecasting of severe acute graft-versus-host disease (aGVHD) after transplantation is a challenging ‘large p, small n’ problem that suffers from nonuniform data sampling. We propose a dynamic probabilistic algorithm, daGOAT, that accommodates sampling heterogeneity, integrates multidimensional clinical data and continuously updates the daily risk score for severe aGVHD onset within a two-week moving window. In the studied cohorts, the cross-validated area under the receiver operator characteristic curve (AUROC) of daGOAT rose steadily after transplantation and peaked at ≥0.78 in both the adult and pediatric cohorts, outperforming the two-biomarker MAGIC score, three-biomarker Ann Arbor score, peri-transplantation features-based models and XGBoost. Simulation experiments indicated that the daGOAT algorithm is well suited for short time-series scenarios where the underlying process for event generation is smooth, multidimensional and where there are frequent and irregular data missing. daGOAT’s broader utility was demonstrated by performance testing on a remotely different task, that is, prediction of imminent human postural change based on smartphone inertial sensor time-series data.
Hemorrhagic cystitis (HC) is a common complication of allogeneic hematopoietic stem cell transplantation (HSCT). The incidence is about 7% to 68%, and some patients have to suffer a long period of frequent, urgent, and painful urination, which brings great pain. This study aimed to analyze risk factors of HC and its effect on patient survival. We collected the medical records of 859 patients who underwent HSCT at our hospital between August 2016 and August 2020. Patients with and without HC were matched using propensity score matching at a 1:1 ratio based on sex, age, and diagnosis, and logistic regression analyses were used to identify factors associated with HC. We used Kaplan–Meier curves to analyze the survival rates of patients in the HC and non-HC groups. We also analyzed the relationship between BK viral load and the occurrence of HC using receiver operating characteristic curve (ROC) analysis. After propensity score matching, there were 131 patients each in the HC and non-HC groups. In the HC group, 89 patients (67.9%) had mild HC (stage II°) and 43 (32.1%) had severe HC (stage III–IV). The median interval between stem cell transplantation and HC development was 31 (3–244) days. Univariate analysis indicated that donor age, hematopoietic stem cell source, HLA, acute graft-versus-host disease, busulfan, anti-thymocyte globulin (ATG), total body irradiation, cytomegalovirus (CMV) (urine), and BK polyomavirus (BKV) (urine) were significantly associated with HC. ATG, CMV (urine), and BKV (urine) were independent risk factors for HC based on the multivariate analysis. The Kaplan–Meier survival analysis showed no significant difference between the HC and non-HC groups (P = .14). The 1- and 2-year survival rates in the HC group were 78.4% and 69.6%, respectively, and the corresponding rates in the non-HC group were 84.4% and 80.7%, respectively. ROC analysis indicated that a urine BKV load of 1 × 107 copies/mL was able to stratify the risk of HC. In conclusion, when the BKV load is >1 × 107, we need to be aware of the potential for the development of HC.
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