201.4: The combination of urine CXCL10 and donor-derived cell free DNA in the non-invasive diagnosis of antibody mediated and T cell mediated rejection in kidney transplantation.
Bibliographic record
Abstract
Introduction: In kidney transplantation today, an allograft biopsy is required to diagnose rejection. Biopsies are invasive and difficult to use as a tool to monitor alloimmune activity over time. While serum creatinine is used clinically, it is neither sensitive nor specific for rejection. While there is increasing evidence that donor derived cell free DNA (dd-cfDNA) performs well as a biomarker of clinical antibody-mediated rejection (AMR), its ability to identify T cell mediated rejection (TCMR, including borderline rejection) remains unclear. In contrast, urine chemokines, such as CXCL10, are well-characterised biomarkers of tubulitis. Due to these complementary properties, we hypothesized that use of these 2 biomarkers together would improve the diagnosis of rejection phenotypes marked predominantly by tubulitis. Method: A retrospective study was conducted whereby 126 kidney transplant biopsies were selected from the Centre Hospitalier de l’Université de Montréal transplant biobank. 120 of 126 biopsies had paired plasma and urine samples collected on the same day while the remaining biopsies had urine and plasma collected within 30 days. Banff 2019 criteria were followed to generate the following diagnostic categories: 20 cases of AMR (including suspicious AMR where 2 of 3 diagnostic criteria for AMR were met), 10 cases of low grade TCMR (Banff 1A or 1B), 7 cases of high grade TCMR (Banff 2B or greater) and 43 cases with normal histology (i,t,v,g and ptc scores=0). Banff borderline diagnoses were excluded. Urine CXCL10 was measured at the Chemokine laboratory, University of Manitoba using the Meso Scale V-Plex assay. Cell free DNA was extracted from EDTA plasma samples and percent of dd-cfDNA measured using the CareDx AlloSeq cfDNA assay (Brisbane, California). Cut-offs of 0.5% dd-cfDNA and 13 pg/ml urine CXCL10 (except for females less than 6 months post-transplant where we used a cut-off of 33 pg/ml) were selected for each assay, respectively. Results: The AUC for AMR (including suspicious AMR, compared to normal histology) was 0.952 (0.893-1000) using dd-cfDNA alone. In contrast, the AUC for urine CXCL10 alone for AMR was 0.595 (0.469-0.722) and increased to 0.969 (0.923-1.000) when combined with dd-cfDNA (p=1.71X10-8) (see Figure 1). When examining high grade TCMR, AUC for dd-cfDNA alone was 0.762 (0.562-0.963). In contrast, AUC for urine CXCL10 alone was 0.681 (0.474-0.888) and increased to 0.792 (0.585-1.000) when dd-cfDNA was added (p=0.16). For low grade TCMR, AUC was 0.577 (0.442-0.711) for dd-cfDNA alone. AUC for urine CXCL10 alone was 0.595 (0.424-0.767) and increased to 0.652 (0.473-0.832) (p=0.32) when combined with dd-cfDNA. Conclusion: Urine CXCL10 is a weaker diagnostic biomarker of AMR compared to dd-cfDNA. In contrast, when evaluating TCMR, there was no clear advantage of one biomarker over the other, though their combination may improve diagnosis. These findings require external validation and prospective studies.Fondation de CHUM.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".