Molecular Analysis of Renal Graft Biopsies: Comparing the Edmonton Molecular Microscope with the NanoString Human Organ Transplant Panel
Bibliographic record
Abstract
Background: The renal transplant biopsy is the diagnostic gold standard and usually evaluated with the continuously expanded and updated Banff classification which is based on on descriptive, empirically-derived criteria and thus lacks precision. High-resolution determination of the graft inflammation by NanoString analysis, which was developed for formalin-fixed paraffin-embedded-derived (FFPE) RNA, should be a sufficient approach for objective molecular diagnosis of renal transplant biopsies and may improve our understanding of graft biology. Methods: We used well-annotated surveillance and indication biopsies from 63 patients whose time-matched second biopsy core had been frozen and analyzed by microarray in the INTERCOM/INTERCOMEX study. After reevaluation according to recent Banff consensus, RNA isolation of the FFPE biopsy was performed and led to sufficient RNA yields in 53 samples which were further processed for NanoString analysis using the nCounter Human Organ Transplant Panel. Results: Morphologically, of the 53 samples analyzed (samples from 2011/12 and 2015), twenty-five patients showed no signs of rejection, twelve had borderline rejection, four showed cellular rejection, seven had humoral rejection, and five presented with combined rejection. Preliminary analysis of gene expression by T-distributed Stochastic Neighbor Embedding (t- SNE), Random Forest and Principal Component Analysis (PCA) showed clear differences between samples with rejection (humoral and cellular) and without rejection. Rejection samples revealed high expression of chemokine ligands compared to rejection-free tissues. A common pattern of samples without rejection and borderline rejection was observed. Our results displayed good correlation with the former molecular microarray-based diagnosis from the INTERCOM/INTERCOMEX study. Conclusions: Molecular diagnostic approach using the NanoString platform may supplement morphological diagnosis of renal grafts especially in unclear cases and thus enhance precision diagnostics with small tissue requirement. Morphological and molecular evaluation in the same biopsy core from FFPE tissue enables direct histologicalmolecular correlation. Additionally, this technology also improves our understanding of pathophysiology in renal and other transplants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".