Gene Expression Profiling in Kidney Transplants with Immune Checkpoint Inhibitor–Associated Adverse Events
Bibliographic record
Abstract
Background and objectives Immune checkpoint inhibitors are increasingly used to treat various malignancies, but their application in patients with kidney transplants is complicated by high allograft rejection rates. Immune checkpoint inhibitor–associated rejection is a novel, poorly understood entity demonstrating overlapping histopathologic features with immune checkpoint inhibitor–associated acute interstitial nephritis, which poses a challenge for diagnosis and clinical management. We sought to improve the understanding of these entities through biopsy-based gene expression analysis. Design, setting, participants, & measurements NanoString was used to measure and compare the expression of 725 immune-related genes in 75 archival kidney biopsies, including a 25-sample discovery cohort comprising pure T cell–mediated rejection and immune checkpoint inhibitor–associated acute interstitial nephritis and an independent 50-sample validation cohort comprising immune checkpoint inhibitor–associated acute interstitial nephritis, immune checkpoint inhibitor–associated T cell–mediated rejection, immune checkpoint inhibitor–associated crescentic GN, drug-induced acute interstitial nephritis, BK virus nephropathy, and normal biopsies. Results Significant molecular overlap was observed between immune checkpoint inhibitor–associated acute interstitial nephritis and T cell–mediated rejection. Nevertheless, IFI27 , an IFN- α– induced transcript, was identified and validated as a novel biomarker for differentiating immune checkpoint inhibitor–associated T cell–mediated rejection from immune checkpoint inhibitor–associated acute interstitial nephritis (validation cohort: P <0.001, area under the receiver operating characteristic curve =100%, accuracy =86%). Principal component analysis revealed heterogeneity in inflammatory gene expression patterns within sample groups; however, immune checkpoint inhibitor–associated T cell–mediated rejection and immune checkpoint inhibitor–associated acute interstitial nephritis both demonstrated relatively more molecular overlap with drug-induced acute interstitial nephritis than T cell–mediated rejection, suggesting potential dominance of hypersensitivity mechanisms in these entities. Conclusions These results indicate that, although there is significant molecular similarity between immune checkpoint inhibitor–associated rejection and acute interstitial nephritis, biopsy-based measurement of IFI27 gene expression represents a potential biomarker for differentiating these entities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".