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Imaging alloreactive T cells provides early warning of organ transplant rejection.

ABSTRACT: Diagnosis of organ transplant rejection relies upon biopsy approaches to confirm alloreactive T cell infiltration in the graft. Immune molecular monitoring is under investigation to screen for rejection, though these techniques have suffered from low specificity and lack of spatial information. ImmunoPET utilizing antibodies conjugated to radioisotopes has the potential to improve early and accurate detection of graft rejection. ImmunoPET is capable of noninvasively visualizing the dynamic distribution of cells expressing specific immune markers in the entire body over time. In this work, we identify and characterize OX40 as a surrogate biomarker for alloreactive T cells in organ transplant rejection and monitor its expression by utilizing immunoPET. In a dual murine heart transplant model that has both syngeneic and allogeneic hearts engrafted in bilateral ear pinna on the recipients, OX40 immunoPET clearly depicted alloreactive T cells in the allograft and draining lymph node that were not observed in their respective isograft counterparts. OX40 immunoPET signals also reflected the subject's immunosuppression level with tacrolimus in this study. OX40 immunoPET is a promising approach that may bridge molecular monitoring and morphological assessment for improved transplant rejection diagnosis.

SUBMITTER: Hirai T

PROVIDER: S-EPMC8410037 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionA decentralized and multi-platform-compatible molecular diagnostic tool for kidney transplant biopsies could improve the dissemination and exploitation of this technology, increasing its clinical impact. As a first step towards this molecular diagnostic tool, we developed and validated a classifier using the genes of the Banff-Human Organ Transplant (B-HOT) panel extracted from a historical Molecular Microscope® Diagnostic system microarray dataset. Furthermore, we evaluated the discriminative power of the B-HOT panel in a clinical scenario.Materials and methodsGene expression data from 1,181 kidney transplant biopsies were used as training data for three random forest models to predict kidney transplant biopsy Banff categories, including non-rejection (NR), antibody-mediated rejection (ABMR), and T-cell-mediated rejection (TCMR). Performance was evaluated using nested cross-validation. The three models used different sets of input features: the first model (B-HOT Model) was trained on only the genes included in the B-HOT panel, the second model (Feature Selection Model) was based on sequential forward feature selection from all available genes, and the third model (B-HOT+ Model) was based on the combination of the two models, i.e. B-HOT panel genes plus highly predictive genes from the sequential forward feature selection. After performance assessment on cross-validation, the best-performing model was validated on an external independent dataset based on a different microarray version.ResultsThe best performances were achieved by the B-HOT+ Model, a multilabel random forest model trained on B-HOT panel genes with the addition of the 6 most predictive genes of the Feature Selection Model (ST7, KLRC4-KLRK1, TRBC1, TRBV6-5, TRBV19, and ZFX), with a mean accuracy of 92.1% during cross-validation. On the validation set, the same model achieved Area Under the ROC Curve (AUC) of 0.965 and 0.982 for NR and ABMR respectively.DiscussionThis kidney transplant biopsy classifier is one step closer to the development of a decentralized kidney transplant biopsy classifier that is effective on data derived from different gene expression platforms. The B-HOT panel proved to be a reliable highly-predictive panel for kidney transplant rejection classification. Furthermore, we propose to include the aforementioned 6 genes in the B-HOT panel for further optimization of this commercially available panel.

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Imaging alloreactive T cells provides early warning of organ transplant rejection.

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