Identification and Cross-Platform Validation of Sparse Molecular Classifiers for Antibody-Mediated and T-Cell–Mediated Rejection After Kidney Transplantation
Bibliographic record
Abstract
Introduction Molecular classifiers are a promising tool to refine the diagnosis of antibody-mediated rejection (ABMR) and T-cell–mediated rejection (TCMR) after kidney transplantation. Despite this potential, the integration of molecular classifiers in transplant clinics has been slow, in part because of the complexity of current assays and lack of a consensus platform. Herein, we aimed to develop and validate sparse molecular classifiers for ABMR and TCMR using allograft tissue. Methods In a discovery cohort of 224 kidney transplant biopsies, lasso regression was applied on microarray gene expression data to derive a molecular classifier for ABMR and TCMR, respectively. Results A 2-gene classifier for ABMR ( PLA1A , GNLY) and a 2-gene classifier for TCMR ( IL12RB1 , ARPC1B) were identified. External validation ( n = 403 biopsies) demonstrated preserved diagnostic accuracy for ABMR (area under the receiver operating characteristic curve [ROC-AUC]: 0.80, 95% confidence interval [CI]: 0.75–0.85) and TCMR (ROC-AUC: 0.83, 95% CI: 0.77–0.89), with the possibility to discriminate between pure and mixed rejection phenotypes. Complementary to their diagnostic potential, the molecular classifiers associated with accelerated graft loss in a second validation cohort ( n = 282 biopsies) and identified allografts at risk for failure with histological lesions that did not reach the Banff thresholds for rejection. The computational approach was further validated using the Banff Human Organ Transplant (B-HOT) gene panel in 2 independent biopsy cohorts that were analyzed on the Nanostring nCounter platform ( n = 66 and n = 80, respectively). Conclusion Rigid variable selection strategies can yield sparse molecular classifiers for allograft rejection phenotypes with preserved accuracy and prognostic value across different molecular diagnostic platforms, which may facilitate their interpretation and clinical implementation.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".