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 (ddcfDNA;CareDx, Brisbane, USA), a non-invasive test that could detect rejection in kidney transplants, was validated using histologic diagnoses. The interpretation of these findings could be difficult due to variable inter- and intra-observer agreement with regards to histologic diagnoses and evolving classifications overtime. The centralized Molecular Microscope(MMDx; Edmonton, CA) tissue gene expression platform may provide increased precision to traditional histology Methods: In this single-center prospective study of 208 biopsies, we present novel data on calibration of cfDNA using simultaneous assessments of all ‘for-cause' and surveillance biopsies with histology(Hx) and MMDx. AUC curves were calculated using the previously published ddcfDNA cut-offs of < 0.21% to rule-out rejection and >1% to rule-in rejection Results: Of 208 biopsies done at a median of 5.8 months post-transplant, 108(52%) were done for allograft dysfunction; 74(36%) for surveillance (due to DSA) and 26(12%) for post-rejection treatment surveillance. There were significant discrepancies between Hx and MMDx; with MMDx(92; 44%) identifying a higher number of rejection cases vs Hx(79; 38%). While MMDx identified a higher number of antibody-mediated rejection cases(65; 31%) than Hx(43; 21%); the opposite was true for T-cell mediated rejection[TCMR; Hx:27(13%) vs MMDx: 13 (6%)]. AUC Curves for cfDNA concentration and prediction of rejection were more robustly correlated with MMDx(AUC=0.830; p<0.001) than with Hx(AUC=0.75; p<0.001). The median cfDNA levels decreased significantly in responders to rejection treatment(median 0.94 to 0.20; p=0.015) vs non-responders(0.76 to 0.82; p=0.25) Conclusions: In this single-center study, for the first time we describe the calibration of ddcfDNA with simultaneous assessment of kidney transplant biopsy with traditional histology and MMDx. We confirmed and expanded on the data from the DART study where a cut-off≥1% was highly sensitive and specific for ruling-in rejection. We report the correlation of cfDNA with response to rejection therapy. We propose that the combination of tissue gene expression using the molecular microscope and blood-based ddcfDNA may add precision to traditional histology and could change future practice and treatment paradigms
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".