Heterogeneity of chronic graft-versus-host disease biomarkers: association with CXCL10 and CXCR3+ NK cells
Bibliographic record
Abstract
Chronic graft-versus-host disease (cGVHD) remains one of the most significant long-term complications after allogeneic blood and marrow transplantation. Diagnostic biomarkers for cGVHD are needed for early diagnosis and may guide identification of prognostic markers. No cGVHD biomarker has yet been validated for use in clinical practice. We evaluated both previously known markers and performed discovery-based analysis for cGVHD biomarkers in a 2 independent test sets (total of 36 cases ≤1 month from diagnosis and 31 time-matched controls with no cGVHD). On the basis of these results, 11 markers were selected and evaluated in 2 independent replication cohorts (total of 134 cGVHD cases and 154 controls). cGVHD cases and controls were evaluated for several clinical covariates, and their impact on biomarkers was identified by univariate analysis. The 2 replications sets were relatively disparate in the biomarkers they replicated. Only sBAFF and, most consistently, CXCL10 were identified as significant in both replication sets. Other markers identified as significant in only 1 replication set included intercellular adhesion molecule 1 (ICAM-1), anti-LG3, aminopeptidase N, CXCL9, endothelin-1, and gelsolin. Multivariate analysis found that all covariates evaluated affected interpretation of the biomarkers. CXCL10 had an increased significance in combination with anti-LG3 and CXCL9, or inversely with CXCR3(+)CD56(bright) natural killer (NK) cells. There was significant heterogeneity of cGVHD biomarkers in a large comprehensive evaluation of cGVHD biomarkers impacted by several covariates. Only CXCL10 strongly correlated in both replication sets. Future analyses for plasma cGVHD biomarkers will need to be performed on very large patient groups with consideration of multiple covariates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".