#6456 DSA-NEGATIVE MICROVASCULAR INFLAMMATION IN KIDNEY TRANSPLANT BIOPSIES: GENE EXPRESSION COMPARISON WITH NATIVE AND TRANSPLANT KIDNEY CONTROLS
Bibliographic record
Abstract
Abstract Background and Aims Microvascular inflammation (MVI) in kidney transplant (KT) biopsies from patients without detectable anti-HLA donor-specific antibodies (DSA) presents a diagnostic and therapeutic dilemma. This study aimed to further understand the significance of these changes by characterizing their molecular phenotype compared to other native and transplant kidney biopsies. Method The NanoString B-HOT panel (770 genes) was used to measure the expression of six literature-derived gene sets in 195 archival FFPE kidney biopsies from four centers, including transplant biopsies with MVI (g+ptc>1) but no detectable DSA (MVI, n = 47), antibody-mediated rejection with DSA (ABMR, n = 42), pure T-cell mediated rejection without DSA (TCMR, n = 25), mixed MVI and TCMR without DSA (MVI+TCMR, n = 47), normal implant biopsies (Normal, n = 11), and native kidney biopsies with either endocapillary proliferative glomerulonephritis (GN, n = 12) or minimal change disease (MCD, n = 11). The evaluated gene sets included transcripts previously associated with ABMR, DSA (DSAST), endothelial injury (ENDAT), TCMR, early injury, and late injury. Gene expression was compared between groups using principal component and class comparison analyses. Results Principal component analysis demonstrated significant molecular overlap between sample groups (Fig. 1A). However, gene set analysis showed lower expression of ABMR-related, DSAST (Fig. 1B), and ENDAT gene sets in DSA-negative MVI compared to ABMR. DSAST and ENDAT gene set expression was similar between MVI, MVI+TCMR, and TCMR groups; but higher than native biopsies (p ≤ 0.002). TCMR and early injury gene set expression was higher in TCMR than all other groups (p ≤ 0.002), except MVI+TCMR. Late injury gene set expression was lower in MVI compared to ABMR, MVI+TCMR, and TCMR groups (p ≤ 0.031); but higher than the Normal and MCD groups (p ≥ 0.016). Conclusion These results suggest that DSA-negative MVI displays a lower expression of ABMR-related genes than ABMR, but similar to MVI+TCMR and higher than native kidney biopsies with or without glomerulonephritis. Further work is underway to evaluate the potential role of non-HLA DSA and recognition of missing self in these cases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".