Digital PCR Improves Sensitivity and Quantification in Monitoring CAR-T Cells in B Cell Lymphoma Patients.

Chimeric antigen receptor T cells (CAR-T) has emerged as a promising therapy, over 60% of patients fail to sustain a long-term response. The underlying factors that leads to the effectiveness of this therapy are not completely understood, CAR-T cell persistence and monitoring seems to be pivotal for ensuring a successful response. Various monitoring methods such as multiparametric flow cytometry (MFC) or quantitative PCR (qPCR) have been applied. Our objective is to develop digital PCR (dPCR) assays for detection and quantification of CAR-T cells, comparing them with MFC and qPCR. Samples taken at different follow-up times from 45 patients treated with CAR-T therapy were analyzed to assess the correlation between the different methodologies. dPCR presented a high correlation with MFC and qPCR (r = 0.97 and r = 0.87, respectively), while offering a higher sensitivity (0.01%) compared to MFC (0.1%) and qPCR (1%). dPCR emerged as an alternative and highly sensitivity method for monitoring CAR-T cell dynamics. This technique is well-suited for implementation in clinical practice as a complementary technique to MFC.

Identification of predictive models including polymorphisms in cytokines genes and clinical variables associated with post-transplant complications after identical HLA-allogeneic stem cell transplantation.

Backgrounds: Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative therapy for hematological malignancies, it can be associated with relevant post-transplant complications. Several reports have shown that polymorphisms in immune system genes are correlated with the development of post-transplant complications. Within this context, this work focuses on identifying novel polymorphisms in cytokine genes and developing predictive models to anticipate the risk of developing graft-versus-host disease (GVHD), transplantation-related mortality (TRM), relapse and overall survival (OS). Methods: Our group developed a 132-cytokine gene panel which was tested in 90 patients who underwent an HLA-identical sibling-donor allo-HSCT. Bayesian logistic regression (BLR) models were used to select the most relevant variables. Based on the cut-off points selected for each model, patients were classified as being at high or low-risk for each of the post-transplant complications (aGVHD II-IV, aGVHD III-IV, cGVHD, mod-sev cGVHD, TRM, relapse and OS). Results: A total of 737 polymorphisms were selected from the custom panel genes. Of these, 41 polymorphisms were included in the predictive models in 30 cytokine genes were selected (17 interleukins and 13 chemokines). Of these polymorphisms, 5 (12.2%) were located in coding regions, and 36 (87.8%) in non-coding regions. All models had a statistical significance of p<0.0001. Conclusion: Overall, genomic polymorphisms in cytokine genes make it possible to anticipate the development all complications studied following allo-HSCT and, consequently, to optimize the clinical management of patients.