Precision transplant pathology

Abstract

Purpose of review: Transplant pathology contributes substantially to personalized treatment of organ allograft recipients. Rapidly advancing next-generation human leukocyte antigen (HLA) sequencing and pathology are enhancing the abilities to improve donor/recipient matching and allograft monitoring.

Recent findings: The present review summarizes the workflow of a prototypical patient through a pathology practice, highlighting histocompatibility assessment and pathologic review of tissues as areas that are evolving to incorporate next-generation technologies while emphasizing critical needs of the field.

Summary: Successful organ transplantation starts with the most precise pratical donor-recipient histocompatibility matching. Next-generation sequencing provides the highest resolution donor-recipient matching and enables eplet mismatch scores and more precise monitoring of donor-specific antibodies (DSAs) that may arise after transplant. Multiplex labeling combined with hand-crafted machine learning is transforming traditional histopathology. The combination of traditional blood/body fluid laboratory tests, eplet and DSA analysis, traditional and next-generation histopathology, and -omics-based platforms enables risk stratification and identification of early subclinical molecular-based changes that precede a decline in allograft function. Needs include software integration of data derived from diverse platforms that can render the most accurate assessment of allograft health and needs for immunosuppression adjustments.

Figure 1.. Overall workflow for precision transplant pathology.

For standard of care (in black), donors and recipients must undergo pre-transplantation assessment for histocompatibility, pathology, and clinical case histories. A suitably matched pair will proceed to transplant and subsequent standard of care monitoring (in black). Post-transplantation, standard monitoring includes electronic medical record review, blood and body fluid assessment for organ function and formation of de novo donor-specific antibody (DSA), and protocol biopsies. A series of domain experts must then contribute their findings to the clinician who ultimately manages the patient care decisions. Workflow improvements (in green) are slowly being implemented in the areas of next generation sequencing (NGS) for HLA typing, proteomic and transcriptomic based assays for early/subclinical molecular changes indicative of disease/rejection progression, templated scoring, and next generation pathology (NGP). The greatest needs for improvement (in red) include algorithms for automated eplet-matching, rapid high-resolution typing for cadaveric donors, reliable reporting of HLA eplet mismatches, and penultimate integrative software that can render a summation of overall allograft health based on multi-platform inputs from experts of various disciplines. Abbreviations: ALT = alanine aminotransferase; AST = aspartate aminotransferase; RT-PCR, reverse transcription polymerase chain reaction; SSOP = sequence-specific oligonucleotide probes.

Figure 2.. Standardized Template for Biopsy Scoring.

(A) Automated software-based Banff kidney classification via application of computational rules and decision tree engine(s). This workflow utilizes a “smart” template that collects key biopsy related parameters (e.g. glomeruli counts, fibrotic area percentages) and (B) translates the morphological data into Banff-component sub-scores via published guidelines. (C) An additional “decision tree”, or inference engine is then layered into the software using plain language to describe the combination of variables that result in a given categorical diagnosis (D). This database-driven inference engine can be combined, layered as needed to model the complexity of the decision. Additionally, AI machine learning can be combined in this step for predictive diagnostic output.

Figure 3.. Next Generation Pathology.

(A) A multiplex labeled kidney section demonstrating quantitative scoring by “tissue-tethered” cytometry. The formalin fixed paraffin embedded specimen stained for DAPI (blue), CD45 (teal), CD34 (green), cytokeratin (magenta), smooth muscle actin (SMA; red) and type III collagen (COL3A1; yellow). (B) Machine vision techniques can identify individual cells and their phenotypic characteristics based on surrounding/overlapping analyte expression (classification mask), which can be used to objectively report the total number of each cell type per mm² of biopsy area or in certain tissue areas (e.g. peri-tubular expression). (C) We localize nuclear (blue), endothelium (CD34⁺, green) and pan-leukocyte (CD45⁺, teal) analytes in possible identified cytoplasmic regions per segmented nucleus, to determine inflammatory cell populations that comprise positive tubulitis and peri-tubular capillaritis expression.