Integrating Pulmonary and Systemic Transcriptomic Profiles to Characterize Lung Injury after Pediatric Hematopoietic Stem Cell Transplant
Bibliographic record
Abstract
Hematopoietic stem cell transplantation (HCT) is potentially curative for numerous malignant and non-malignant diseases but can lead to lung injury due to chemoradiation toxicity, infection, and immune dysregulation. Bronchoalveolar lavage (BAL) is the most commonly used procedure for diagnostic sampling of the lung but is invasive, cannot be performed in medically fragile patients, and is challenging to perform serially. We previously showed that BAL transcriptomes representing pulmonary inflammation and cellular injury can phenotype post-HCT lung injury and predict mortality outcomes. However, whether peripheral blood testing is a suitable minimally-invasive surrogate for pulmonary sampling in the HCT population remains unknown. To address this question, we compared 210 paired BAL and peripheral blood transcriptomes obtained from 166 pediatric HCT patients at 27 children's hospitals. BAL and blood mRNA abundance showed minimal overall correlation at the level of individual genes, gene set enrichment scores, imputed cell fractions, and T- and B-cell receptor clonotypes. Instead, we identified significant site-specific transcriptional programs. In BAL, expression of innate and adaptive immune pathways was tightly co-regulated with expression of epithelial mesenchymal transition and hypoxia pathways, and these signatures were associated with mortality. In contrast, in blood, expression of endothelial injury, DNA repair, and cellular metabolism pathways was associated with mortality. Integration of paired BAL and blood transcriptomes dichotomized patients into two groups, of which one group showed twice the rate of hypoxia and significantly worse outcomes within 7 days of enrollment. These findings reveal a compartmentalized injury response, where BAL and peripheral blood transcriptomes provide distinct but complementary insights into local and systemic mechanisms of post-HCT lung injury.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".