Distinct Molecular Processes Mediate Donor-derived Cell-free DNA Release From Kidney Transplants in Different Disease States
Bibliographic record
Abstract
BACKGROUND: Among all biopsies in the Trifecta-Kidney Study ( ClinicalTrials.gov NCT04239703), elevated plasma donor-derived cell-free DNA (dd-cfDNA) correlated most strongly with molecular antibody-mediated rejection (AMR) but was also elevated in other states: T cell-mediated rejection (TCMR), acute kidney injury (AKI), and some apparently normal biopsies. The present study aimed to define the molecular correlates of plasma dd-cfDNA within specific states. METHODS: Dd-cfDNA was measured by the Prospera test. Molecular rejection and injury states were defined using the Molecular Microscope system. We studied the correlation between dd-cfDNA and the expression of genes, transcript sets, and classifier scores within specific disease states, and compared AMR, TCMR, and AKI to biopsies classified as normal and no injury (NRNI). RESULTS: In all 604 biopsies, dd-cfDNA was elevated in AMR, TCMR, and AKI. Within AMR biopsies, dd-cfDNA correlated with AMR activity and stage. Within AKI, the correlations reflected acute parenchymal injury, including cell cycling. Within biopsies classified as MMDx Normal and archetypal No injury (NRNI), dd-cfDNA still correlated significantly with rejection- and injury-related genes. TCMR activity (eg, the TCMR Prob classifier) correlated with dd-cfDNA, but within TCMR biopsies, top gene correlations were complex and not the top TCMR-selective genes. CONCLUSIONS: In kidney transplants, elevated plasma dd-cfDNA is associated with 3 distinct molecular states in the donor tissue: AMR, recent parenchymal injury (including cell cycling), and TCMR, potentially complicated by parenchymal disruption. Moreover, subtle rejection- and injury-related changes in the donor tissue can contribute to dd-cfDNA elevations in transplants considered to have no rejection or injury.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".