Alemtuzumab and CXCL9 levels predict likelihood of sustained engraftment after reduced-intensity conditioning HCT
Bibliographic record
Abstract
Overall survival after reduced-intensity conditioning (RIC) allogeneic hematopoietic cell transplantation (HCT) using alemtuzumab, fludarabine, and melphalan is associated with high rates of mixed chimerism (MC) and secondary graft failure (GF). We hypothesized that peritransplantation alemtuzumab levels or specific patterns of inflammation would predict these risks. We assessed samples from the Bone Marrow Transplant Clinical Trials Network 1204 (NCT01998633) to study the impact of alemtuzumab levels and cytokine patterns on MC and impending or established secondary GF (defined as donor chimerism <5% after initial engraftment and/or requirement of cellular intervention). Thirty-three patients with hemophagocytic lymphohistiocytosis (n = 25) and other IEIs (n = 8) who underwent HCTs with T-cell-replete grafts were included. Patients with day 0 alemtuzumab levels ≤0.32 μg/mL had a markedly lower incidence of MC, 14.3%, vs 90.9% in patients with levels >0.32 μg/mL (P = .008). Impending or established secondary GF was only observed in patients with day 0 alemtuzumab levels >0.32 μg/mL (P = .08). Unexpectedly, patients with impending or established secondary GF had lower CXCL9 levels. The cumulative incidence of impending or established secondary GF in patients with a day 14+ CXCL9 level ≤2394 pg/mL (day 14+ median) was 73.6% vs 0% in patients with a level >2394 pg/mL (P = .002). CXCL9 levels inversely correlated with alemtuzumab levels. These data suggest a model in which higher levels of alemtuzumab at day 0 deplete donor T cells, inhibit the graft-versus-marrow reaction (thereby suppressing CXCL9 levels), and adversely affect sustained engraftment in the nonmyeloablative HCT setting. This trial was registered at www.clinicaltrials.gov as #NCT01998633.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".