The adverse events of haematopoietic stem cell transplantation are associated with gene polymorphism within human leukocyte antigen region

Preliminary Data on SNP of Transplantation-Related Genes after Haploidentical Stem Cell Transplantation

Background: Hematopoietic stem cell transplantation (HSCT) is one of the mainstream treatments for patients with hematologic malignancies. The matching status of human leukocyte antigen (HLA) between the donor and recipient is highly related to the outcomes of HSCT. Haploidentical HSCT (haplo-HSCT) has emerged as a type of HSCT for patients who cannot find a fully HLA-matched donor. In this study, we investigated whether the single nucleotide polymorphisms (SNPs) of the HLA-related genes and the genes encoding co-stimulatory molecules located on the non-HLA region are related to the outcomes of haplo-HSCT. Methods: The genomic DNAs of 24 patients and their respective donors were isolated from the peripheral blood obtained before performing haplo-HSCT. A total of 75 SNPs of the HLA-related genes (HCP5, NOTCH4, HLA-DOA, LTA, HSPA1L, BAG6, RING1, TRIM27, and HLA-DOB) and the genes located in the non-HLA genes involved in co-stimulatory signaling (CTLA4, TNFSF4, CD28, and PDCD1) were selected to explore their relationship with the outcomes after haplo-HSCT, including graft-versus-host disease, survival status, and relapse. Results: Our data revealed that specific donor or patient SNPs, including rs79327197 of the HLA-DOA gene, rs107822 and rs213210 of the RING1 gene, rs2523676 of the HCP5 gene, rs5742909 of the CTLA4 gene, rs5839828 and rs36084323 of the PDCD1 gene, and rs1234314 of the TNFSF4 gene, were significantly related to the development of adverse outcomes post-haplo-HSCT. Conclusions: These SNPs may play important roles in post-transplant immune response that can be considered during the selection of suitable donors.

Prediction of Acute Cardiac Rejection Based on Gene Expression Profiles

Acute cardiac rejection remains a significant challenge in the post-transplant period, necessitating meticulous monitoring and timely intervention to prevent graft failure. Thus, the goal of the present study was to identify novel biomarkers involved in acute cardiac rejection, paving the way for personalized diagnostic, preventive, and treatment strategies. A total of 809 differentially expressed genes were identified in the GSE150059 dataset. We intersected genes selected by analysis of variance, recursive feature elimination, least absolute shrinkage and selection operator, and random forest classifier to identify the most relevant genes involved in acute cardiac rejection. Thus, HCP5, KLRD1, GZMB, PLA1A, GNLY, and KLRB1 were used to train eight machine learning models: random forest, logistic regression, decision trees, support vector machines, gradient boosting machines, K-nearest neighbors, XGBoost, and neural networks. Models were trained, tested, and validated on the GSE150059 dataset (MMDx-based diagnosis of rejection). Eight algorithms achieved great performance in predicting acute cardiac rejection. However, all machine learning models demonstrated poor performance in two external validation sets that had rejection diagnosis based on histology: merged GSE2596 and GSE4470 dataset and GSE9377 dataset, thus highlighting differences between these two methods. According to SHAP and LIME, KLRD1 and HCP5 were the most impactful genes.

Screening of single nucleotide polymorphisms within HLA region related to hematopoietic stem cell transplantation using MassARRAY technology

A growing number of studies showed that single nucleotide polymorphisms (SNPs) in the human leukocyte antigen (HLA)-related genes were associated with the outcome of hematopoietic stem cell transplantation (HSCT). Thus, other SNPs located nearby the classical HLA genes must be considered in HSCT. We evaluated the clinical feasibility of MassARRAY by comparing to Sanger sequencing. The PCR amplicons with each one of the 17 loci that were related to the outcomes of HSCT published by our previous study were transferred onto a SpectroCHIP Array for genotyping by mass spectrometry. The sensitivity of MassARRAY was 97.9% (614/627) and the specificity was 100% (1281/1281), where the positive predictive value (PPV) was 100% (614/614) and the negative predictive value (NPV) was 99.0% (1281/1294). MassARRAY is high-throughput, which can accurately analyze multiple SNPs at the same time. Based on these properties, we proposed that it could be an efficient method to match the genotype between the graft and the recipient before transplantation.

The Association Between Single-Nucleotide Polymorphisms of Co-Stimulatory Genes Within Non-HLA Region and the Prognosis of Leukemia Patients With Hematopoietic Stem Cell Transplantation

To avoid graft rejection, the hematopoietic stem cells with matched classical human leukocyte antigen (HLA) alleles are the primary choice for clinical allogeneic transplantation. However, even if the fully HLA-matched hematopoietic stem cells are used for transplantation, some patients still have poor prognosis after hematopoietic stem cell transplantation (HSCT), suggesting that the HLA system was not the only determinant of the outcomes of HSCT. In this study, we investigated whether the single-nucleotide polymorphisms (SNPs) of the co-stimulatory genes within non-HLA regions were related to the outcomes of HSCT. The genomic DNAs of 163 patients who had acute leukemia and received HSCT and their respective donors were collected for analysis. Thirty-four SNPs located in the four co-stimulatory genes including cytotoxic T-lymphocyte associated protein 4 (CTLA4), CD28, tumor necrosis factor ligand superfamily 4 (TNFSF4), and programmed cell death protein 1 (PDCD1) were selected to explore their relationship with the adverse outcomes after transplantation, including mortality, cytomegalovirus infection, graft-versus-host disease, and relapse. Our results revealed that nine SNPs in the CTLA4 gene, five SNPs in the PDCD1 gene, two SNPs in the TNFSF4 gene, and four SNPs in the CD28 gene were significantly associated with the occurrence of adverse outcomes post-HSCT. These SNPs may play important roles in immune response to allografts post-HSCT and can be the targets for developing strategy to identify appropriate donors.