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Abstract: FR-PO788

Development of a Tissue-Based Classifier of Allograft Inflammation Using Imaging Mass Cytometry

Session Information

Category: Transplantation

  • 2002 Transplantation: Clinical

Authors

  • Alexander, Mariam P., Mayo Clinic Minnesota, Rochester, Minnesota, United States
  • Zaidi, Mark, University of Toronto, Toronto, Ontario, Canada
  • Stegall, Mark D., Mayo Clinic Minnesota, Rochester, Minnesota, United States
  • Bentall, Andrew J., Mayo Clinic Minnesota, Rochester, Minnesota, United States
  • Mckee, Trevor David, Deciphex Ltd, Dublin, Dublín, Ireland
  • Taner, Timucin, Mayo Clinic Minnesota, Rochester, Minnesota, United States
Background

Molecular phenotyping of allograft inflammation has improved both diagnostic accuracy & our understanding of the heterogeneity of rejection. Current molecular techniques lack histological correlation & spatial dimensionality. Our goal was to use Imaging Mass Cytometry (IMC) to develop a tool to accurately predict the cause of allograft inflammation.

Methods

Our cohort included biopsies of rejection, BK nephropathy, pyelonephritis & normal kidneys. Using a panel of 28 markers, IMC images were processed by the Hyperion imaging system. Details of analysis are in Fig 1A. Cell segmentation was performed using Universal StarDist for Qupath. Cell classification was performed based on mean intensity threshold.

Results

139 regions of interest (ROI) were processed. Violin plots ensured there were measurable differences in known markers associated with each allograft inflammation category. Distribution of percent positive scoring of immune cells are seen in the heatmap (1B) [e.g.: cellular & mixed rejection cases enriched in CD45+, HLA-DR+ cells and CD4+ memory T cells]. The trained regularized gradient boosting classifier model XGBoost was used to predict the allograft inflammation category for all cells, ROIs and each original histological diagnosis. (Fig 1C &D). The trained model accurately predicted the allograft inflammation category for each cell with an accuracy of 64.3%. When using the mean intensity parameter of each cell, the classifier accuracy improved to 87.8% in predicting the type of renal allograft inflammation, independent of ROI. The accuracy improved to 90.9% when dimension of intracellular spatial features (proximity metrics) were added to the algorithm. Granzyme, CD68 and Vista were the three most important markers in achieving this high accuracy.

Conclusion

Using highly multiplexed imaging of renal allograft biopsies with subcellular resolution by IMC we have developed a novel classifier of allograft inflammation, which demonstrates high diagnostic accuracy.

Figure 1