Investigating Blood-Based, Cell-Specific Biomarkers of Acute Cardiac Allograft Rejection
Bibliographic record
Abstract
Introduction: Cardiac transplantation is the main intervention for patients with end-stage heart failure. Despite great improvements in maintenance immunosuppressive therapies, acute allograft rejection remains a clinical problem. Timely detection of moderate rejection allows for treatment to be modified, preventing organ damage, graft failure and patient death. Regular monitoring for rejection is thus crucial. The endomyocardial biopsy (EMB) is the current standard for monitoring the allograft, but the procedure is highly invasive and costly. Consequently, there is great interest in developing blood-based biomarker tests to monitor for allograft rejection. Blood is a complex tissue, however, and accounting for its dynamic cellular heterogeneity is likely key to identifying robust biomarkers. We investigated whether integrating cellular composition estimates can lead to better performing biomarkers in the context of acute cardiac allograft rejection. Methods: Genome-wide transcript abundance was assayed from PAXgene peripheral whole blood samples obtained from patients undergoing biopsy-confirmed acute allograft rejection (≥2R; n = 29) or not (n = 198), using Affymetrix Human Gene 1.1 ST microarrays. The cellular composition of the blood samples was inferred from their gene expression profiles. Elastic net classifier panels were then identified using the whole blood gene expression, either unadjusted for cellular composition, adjusted for cellular composition for all cell types, or all but one cell type. Out-of-sample performance of the various models was estimated using stratified 5-fold cross-validation. Results: Best cross-validation performance (AUC = 0.75) was achieved by an NK cell-specific classifier panel. Both cell-adjusted and cell-specific classifiers outperformed those identified using unadjusted whole blood gene expression data. Conclusion: Cell-adjusted/specific biomarker panels outperformed those derived from unadjusted whole blood gene expression. Best performance was achieved by an NK cell-specific biomarker panel. The method presented here is a promising way to incorporate cellular composition in the context of biomarker discovery from gene expression in tissue admixtures.Figure
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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 | 0.002 | 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".