Correlation of Donor-derived Cell-free DNA With Histology and Molecular Diagnoses of Kidney Transplant Biopsies
Bibliographic record
Abstract
BACKGROUND: Circulating donor-derived cell-free DNA (cfDNA), a minimally invasive diagnostic tool for kidney transplant rejection, was validated using traditional histology. The molecular microscope diagnostic system (MMDx) tissue gene expression platform may provide increased precision to traditional histology. METHODS: In this single-center prospective study of 208 biopsies (median = 5.8 mo) posttransplant, we report on the calibration of cfDNA with simultaneous biopsy assessments using MMDx and histology by area under the curve (AUC) analyses for optimal criterion, as well as for, previously published cfDNA cutoffs ≤ 0.21% to "rule-out" rejection and ≥1% to "rule-in" rejection. RESULTS: There were significant discrepancies between histology and MMDx, with MMDx identifying more antibody-mediated rejection (65; 31%) than histology (43; 21%); the opposite was true for T cell-mediated rejection [TCMR; histology: 27 (13%) versus MMDx: 13 (6%)]. Most of the TCMR discrepancies were seen for histologic borderline/1A TCMR. AUC for cfDNA and prediction of rejection were slightly better with MMDx (AUC = 0.80; 95% CI: 0.74-0.86) versus histology (AUC = 0.75; 95% CI: 0.69-0.81). A cfDNA ≤ 0.21% had similar sensitivity (~91%) to "rule-out" rejection by histology and MMDx. Specificity was slightly higher with MMDx (92%) compared with histology (85%) to "rule-in" rejection using cfDNA criterion ≥1%. Strong positive quantitative correlations were observed between cfDNA scores and molecular acute kidney injury for both "rejection" and "nonrejection" biopsies. CONCLUSIONS: Molecular diagnostics using tissue gene expression and blood-based donor-derived cell-free DNA may add precision to some cases of traditional histology. The positive correlation of cfDNA with molecular acute kidney injury suggests a dose-dependent association with tissue injury irrespective of rejection characteristics.
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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 | 0.005 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".