Transcriptional Changes in Kidney Allografts with Histology of Antibody-Mediated Rejection without Anti-HLA Donor-Specific Antibodies
Bibliographic record
Abstract
Significance Statement Donor-specific anti-HLA antibodies (HLA-DSAs) are often not detectable in serum of kidney allograft recipients whose biopsies display histology of antibody-mediated rejection (ABMR), which creates uncertainty in clinical decision making. The authors show that ABMR histology associates with a distinct transcriptional profile that is independent of the presence of HLA-DSAs, although the presence of HLA-DSAs is also an independent risk factor for graft failure after ABMR histology. However, molecular assessment of allograft biopsy specimens does not elucidate the underlying cause of ABMR histology, and these findings indicate that therapeutic decisions should not be based solely on the histologic and molecular presentation. Future studies should work toward identifying and targeting the underlying stimulus of ABMR histology. Background Circulating donor-specific anti-HLA antibodies (HLA-DSAs) are often absent in serum of kidney allograft recipients whose biopsy specimens demonstrate histology of antibody-mediated rejection (ABMR). It is unclear whether cases involving ABMR histology without detectable HLA-DSAs represent a distinct clinical and molecular phenotype. Methods In this multicenter cohort study, we integrated allograft microarray analysis with extensive clinical and histologic phenotyping from 224 kidney transplant recipients between 2011 and 2017. We used the term ABMR histology for biopsy specimens that fulfill the first two Banff 2017 criteria for ABMR, irrespective of HLA-DSA status. Results Of 224 biopsy specimens, 56 had ABMR histology; 26 of these (46.4%) lacked detectable serum HLA-DSAs. Biopsy specimens with ABMR histology showed overexpression of transcripts mostly related to IFN γ -induced pathways and activation of natural killer cells and endothelial cells. HLA-DSA–positive and HLA-DSA–negative biopsy specimens with ABMR histology displayed similar upregulation of pathways and enrichment of infiltrating leukocytes. Transcriptional heterogeneity observed in biopsy specimens with ABMR histology was not associated with HLA-DSA status but was caused by concomitant T cell–mediated rejection. Compared with cases lacking ABMR histology, those with ABMR histology and HLA-DSA had higher allograft failure risk (hazard ratio [HR], 7.24; 95% confidence interval [95% CI], 3.04 to 17.20) than cases without HLA-DSA (HR, 2.33; 95% CI, 0.85 to 6.33), despite the absence of transcriptional differences. Conclusions ABMR histology corresponds to a robust intragraft transcriptional signature, irrespective of HLA-DSA status. Outcome after ABMR histology is not solely determined by the histomolecular presentation but is predicted by the underlying etiologic factor. It is important to consider this heterogeneity in further research and in treatment decisions for patients with ABMR histology.
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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 | 0.001 |
| 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) | 0.001 | 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".