Transcriptomic Signatures of Antibody-mediated Rejection in Early Biopsies With Negative Histology in HLA-incompatible Kidney Transplantation
Bibliographic record
Abstract
Background. Presensitized patients with circulating donor-specific antibodies (DSAs) before transplantation are at risk for antibody-mediated rejection (AMR). Peritransplant desensitization mitigates but does not eliminate the alloimmune response. We examined the possibility that subthreshold AMR activity undetected by histology could be operating in some early biopsies. Methods. Transcriptome of kidney allograft biopsies performed within the first month in presensitized patients (DSA + ) who had received desensitization and did not develop active/probable AMR by histology (R – ) was compared with biopsies showing active/probable AMR (R + /DSA + ). As negative controls, biopsies without rejection by histology in patients without DSA at transplantation were used (R – /DSA – ). RNA sequencing from biopsies selected from the biobank was used in cohort 1 (n = 32) and microarray, including the molecular microscope (Molecular Microscope Diagnostic System [MMDx]) algorithm, in recent cohort 2 (n = 30). Results. The transcriptome of R – /DSA + was similar to R + /DSA + as these groups differed in 14 transcripts only. Contrarily, large differences were found between both DSA + groups and negative controls. Fast gene set enrichment analyses showed upregulation of the immune system in both DSA + groups (gene ontology terms: adaptive immune response, humoral immune response, antigen receptor-mediated signaling, and B-cell receptor signaling or complement activation) when compared with negative controls. MMDx assessment in cohort 2 classified 50% of R – /DSA + samples as AMR and found no differences in AMR molecular scores between R + and R – DSA + groups. In imlifidase desensitization, MMDx series showed a gradual increase in AMR scores over time. Conclusions. Presensitized kidney transplant recipients exhibited frequent molecular calls of AMR in biopsy-based transcript diagnostics despite desensitization therapy and negative histology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".