Antibody-mediated Rejection Without Detectable Donor-specific Antibody Releases Donor-derived Cell-free DNA: Results From the Trifecta Study
Bibliographic record
Abstract
BACKGROUND: Trifecta (ClinicalTrials.gov #NCT04239703) is a prospective trial defining relationships between donor-derived cell-free DNA (dd-cfDNA), donor-specific antibody (DSA), and molecular findings in kidney transplant biopsies. Previous analyses of double results showed dd-cfDNA was strongly associated with rejection-associated molecules in the biopsy. The present study analyzed the triple results in 280 biopsies, focusing on the question of dd-cfDNA levels in DSA-negative antibody-mediated rejection (AMR). METHODS: Molecular Microscope Diagnostic System biopsy testing was performed at Alberta Transplant Applied Genomics Centre, dd-cfDNA testing at Natera, Inc, and central HLA antibody testing at One Lambda Inc. Local DSA and histologic diagnoses were assigned per center standard-of-care. RESULTS: DSA was frequently negative in both molecular (56%) and histologic (51%) AMR. DSA-negative AMR had slightly less molecular AMR activity and histologic peritubular capillaritis than DSA-positive AMR. However, all AMRs-DSA-positive or -negative-showed elevated %dd-cfDNA. There was no association between dd-cfDNA and DSA in biopsies without rejection. In AMR, %dd-cfDNA ≥1.0 was more frequent (75%) than DSA positivity (44%). In logistic regression, dd-cfDNA percent (area under the curve [AUC] 0.85) or quantity (AUC 0.86) predicted molecular AMR better than DSA (AUC 0.66). However, the best predictions incorporated both dd-cfDNA and DSA, plus time posttransplant (AUC 0.88). CONCLUSIONS: DSA-negative AMR has moderately decreased mean molecular and histologic AMR-associated features compared with DSA-positive AMR, though similarly elevated dd-cfDNA levels. In predicting AMR at the time of indication biopsies in this population, dd-cfDNA is superior to DSA, reflecting the prevalence of DSA-negative AMR, but the optimal predictions incorporated both dd-cfDNA and DSA.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".