Molecular Diagnosis of Antibody-Mediated Rejection in Human Kidney Transplants
Bibliographic record
Abstract
Antibody-mediated rejection is the major cause of kidney transplant failure, but the histology-based diagnostic system misses most cases due to its requirement for C4d positivity. We hypothesized that gene expression data could be used to test biopsies for the presence of antibody-mediated rejection. To develop a molecular test, we prospectively assigned diagnoses, including C4d-negative antibody-mediated rejection, to 403 indication biopsies from 315 patients, based on histology (microcirculation lesions) and donor-specific HLA antibody. We then used microarray data to develop classifiers that assigned antibody-mediated rejection scores to each biopsy. The transcripts distinguishing antibody-mediated rejection from other conditions were mostly expressed in endothelial cells or NK cells, or were IFNG-inducible. The scores correlated with the presence of microcirculation lesions and donor-specific antibody. Of 45 biopsies with scores>0.5, 39 had been diagnosed as antibody-mediated rejection on the basis of histology and donor-specific antibody. High scores were also associated with unanimity among pathologists that antibody-mediated rejection was present. The molecular score also strongly predicted future graft loss in Cox regression analysis. We conclude that microarray assessment of gene expression can assign a probability of ABMR to transplant biopsies without knowledge of HLA antibody status, histology, or C4d staining, and predicts future failure.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".