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- Title
A Decentralized Kidney Transplant Biopsy Classifier for Transplant Rejection Developed Using Genes of the Banff-Human Organ Transplant Panel.
- Authors
van Baardwijk, Myrthe; Cristoferi, Iacopo; Ju, Jie; Varol, Hilal; Minnee, Robert C.; Reinders, Marlies E. J.; Li, Yunlei; Stubbs, Andrew P.; Clahsen-van Groningen, Marian C.
- Abstract
Introduction: A 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 Methods: Gene 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. Results: The 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. Discussion: This 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.
- Subjects
BANFF (Alta.); KIDNEY transplantation; TRANSPLANTATION of organs, tissues, etc.; GRAFT rejection; RENAL biopsy; FEATURE selection; KIDNEY failure; ALLOIMMUNITY; MOLECULAR diagnosis
- Publication
Frontiers in Immunology, 2022, Vol 13, p1
- ISSN
1664-3224
- Publication type
Article
- DOI
10.3389/fimmu.2022.841519