Sibling versus Unrelated Donor Allogeneic Hematopoietic Cell Transplantation for Chronic Myelogenous Leukemia: Refined HLA Matching Reveals More Graft-versus-Host Disease but not Less Relapse
Bibliographic record
Abstract
Unrelated donor (URD) hematopoietic cell transplantation (HCT) can eradicate chronic myelogenous leukemia (CML). It has been postulated that greater donor-recipient histoincompatibility can augment the graft-versus-leukemia (GVL) effect. We previously reported similar, but not equivalent, outcomes of URD versus sibling donor HCT for CML using an older, less precise classification of HLA matching. Here, we used our recently refined HLA-matching classification, which is suitable for interpretation when complete allele-level typing is unavailable, to reanalyze outcomes of previous HCT for CML. We found that using our new matching criteria identifies substantially more frequent mismatching than older, less precise "6 of 6 antigen-matched" URD-HCT. Under the new criteria, only 37% of those previously deemed "HLA- matched" were HLA well matched, and 44% were partially matched. Using our refined matching criteria confirms the greater risk of graft failure in partially matched or mismatched URD-recipient pairs compared with either sibling or well-matched URD-recipient pairs. Acute and chronic graft-versus-host disease (aGVHD, cGVHD) are significantly more frequent with all levels of recategorized URD HLA matching. Importantly, overall survival (OS) and leukemia-free survival (LFS) remain significantly worse after URD-HCT at any matching level. No augmented GVL effect accompanied URD HLA mismatch. Compared with sibling donor transplants, we observed only marginally increased (not statistically significant) risks of relapse in well-matched, partially matched, and mismatched URD-HCT. These data confirm the applicability of revised HLA-matching scheme in analyzing retrospective data sets when fully informative, allele-level typing is unavailable. In this analysis, greater histoincompatibility can augment GVHD, but does not improve protection against relapse; thus the best donor remains the most closely matched donor.
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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 | 0.001 | 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".