The Effects of Clazakizumab on Peripheral Blood and Kidney Transcriptomes in Patients With Late Antibody-Mediated Rejection
Bibliographic record
Abstract
Introduction: There are no licensed treatments for antibody-mediated rejection (AMR), a major cause of late kidney allograft loss. Clazakizumab (CLZ), an interleukin (IL)-6-neutralizing antibody, showed potential efficacy in a phase 2 trial in late AMR, with a reduction in donor-specific antibodies (DSAs) and kidney molecular microscope diagnostic system (MMDx) AMR score, but the underpinning mechanisms are unclear. Methods: Using peripheral blood transcriptomics, we identified a decrease in IL-6-associated "JAK-STAT signaling" pathway genes with CLZ, and a reduction in gene modules that enriched for T follicular helper cell and activated platelet signatures, cells that contribute to DSA generation and inflammatory responses to DSA respectively. However, responses were variable, and some patients showed a rebound in the expression of inflammatory signatures with long-term CLZ treatment, indicating variability in the efficacy of IL-6 antagonism. One peripheral blood gene module significantly correlated with kidney MMDx AMR score and enriched for monocyte signature genes, as well as "Fc gamma receptor-mediated phagocytosis" and "leukocyte transendothelial migration" gene sets, suggesting that cells activated by DSAs can be detected in peripheral blood. In the kidney, CLZ-treatment was associated with a significant reduction in a damaged tubule gene signature and preservation of podocyte signatures. We also found a kidney plasma cell gene-rich module that positively correlated with circulating DSAs; however, this was not significantly downregulated by CLZ. Conclusion: Overall, our results provide mechanistic insights into the effects and limitations, of IL-6 neutralization in humans in the context of AMR.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".