P168 An algorithm to increase the informativeness of chimerism analysis in stem cell transplantation

Abstract

Aim Chimerism analysis is an important tool for engraftment monitoring after hematopoietic stem cell transplantation (HSCT). It could have a highly predictive value if used appropriately. Therefore, we aim to develop a criteria for classification of patients according to their chimerism profile in order to detect earlier upcoming post-transplant complications. Methods In this retrospective study we have included 67 patients with hematologic malignancies. All patients were selected to be transplanted from HLA 10/10 compatible donor in order to decrease the role of HLA compatibility in post-HSCT period. The chimerism analysis was performed by a PCR-STR method. Donor cells ⩾99% were considered as a full donor chimerism (FDC). We used an intra-laboratory criteria to classify patients into four groups according to the dynamics of their donor chimerism (DC). The classification aimed to decrease the influence of the technical aspects of the STR platform, as well as its sensitivity, and increase its informativeness. Results Mortality (p = 0.004) and disease relapse (p Conclusions When individual drop-downs of the chimerism were taken into consideration, its monitoring seems to have a highly predictive value for disease relapse and mortality. Thus, regular monitoring of patients’ hematopoiesis with precise and sensitive methodology would greatly benefit the early detection of upcoming relapse or other life-threatening complication.