Effect of felzartamab on the molecular phenotype of antibody-mediated rejection in kidney transplant biopsies
Bibliographic record
Abstract
A recent randomized controlled trial demonstrated that treatment with anti-CD38 monoclonal antibody felzartamab suppressed antibody-mediated rejection (ABMR) in kidney transplant patients but with recurrence after treatment in some patients. Here we examined the molecular effects of 6 months of felzartamab treatment on biopsies from the trial using genome-wide microarray analysis, comparing pretreatment, end-of-treatment (week 24) and posttreatment (week 52) biopsies from ten patients treated with felzartamab and ten patients in the placebo group. Felzartamab reduced molecular ABMR activity scores in all nine patients with baseline ABMR activity, selectively suppressing interferon gamma-inducible and natural killer cell transcripts, with minimal effect on ABMR stage-related endothelial transcripts. Suppression was often incomplete when ABMR activity was intense, and molecular recurrence was nearly universal by week 52. However, we also found that felzartamab had parenchymal benefits at week 52, slowing the trajectories of molecular injury scores beyond the treatment period, suggesting that suppression of ABMR activity could potentially slow future progression to kidney failure. These data provide preliminary molecular insights into the effects of CD38-directed treatment for ABMR, which have the potential to inform future therapeutic strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".