Donor-Recipient Matching for KIR Genotypes Reduces Chronic GVHD and Missing Inhibitory KIR Ligands Protect against Relapse after Myeloablative, HLA Matched Hematopoietic Cell Transplantation
Bibliographic record
Abstract
BACKGROUND: Allogeneic hematopoietic cell transplantation (HCT) can be curative for many hematologic diseases. However, complications such as graft-versus-host disease (GVHD) and relapse of primary malignancy remain significant and are the leading causes of morbidity and mortality. Effects of killer Ig-like receptors (KIR)-influenced NK cells on HCT outcomes have been extensively pursued over the last decade. However, the relevance of the reported algorithms on HLA matched myeloablative HCT with rabbit antithymocyte globulin (ATG) is used for GVHD prophylaxis remains elusive. Here we examined the role of KIR and KIR-ligands of donor-recipient pairs in modifying the outcomes of ATG conditioned HLA matched sibling and unrelated donor HCT. METHODS AND FINDINGS: The study cohort consisted of 281 HLA matched sibling and unrelated donor-recipient pairs of first allogeneic marrow or blood stem cell transplantation allocated into 'discovery' (135 pairs) and 'validation' (146 pairs) cohorts. High resolution HLA typing was obtained from the medical charts and KIR gene repertoires were obtained by a Luminex® based SSO method. All surviving patients were followed-up for a minimum of two years. KIR and HLA class I distributions of HCT pairs were stratified as per applicable definitions and were tested for their association with cause specific outcomes [acute GVHD grade II-IV (aGVHD), chronic GVHD needing systemic therapy (cGVHD) and relapse] using a multivariate competing risks regression model as well as with survival outcomes [relapse-free survival (RFS), cGVHD & relapse free survival (cGRFS) and overall survival (OS)] by multivariate Cox proportional hazards regression model. A significant association between KIR genotype mismatching (KIR-B/x donor into KIR-AA recipient or vice versa) and cGVHD was found in both discovery (p = 0.001; SHR = 2.78; 95%CI: 1.50-5.17) and validation cohorts (p = 0.005; SHR = 2.61; 95%CI: 1.33-5.11). High incidence of cGVHD associated with KIR genotype mismatching was applicable to both sibling and unrelated donors and was specific to recipients who had one or two C1 bearing HLA-C epitopes (HLA-C1/x, p = 0.001; SHR = 2.40; 95%CI: 1.42-4.06). When compared with KIR genotype mismatched transplants, HLA-C1/x patients receiving grafts from KIR genotype matched donors had a significantly improved cGRFS (p = 0.013; HR = 1.62; 95%CI: 1.11-2.39). Although there was no effect of KIR genotype matching on survival outcomes, a significantly reduced incidence of relapse (p = 0.001; SHR = 0.22; 95%CI: 0.10-0.54) and improved relapse-free survival (p = 0.038; HR = 0.40; 95%CI: 0.17-0.95) was observed with one or more missing ligands for donor inhibitory KIR among the recipients of unrelated donor transplants. CONCLUSIONS: The present study for the first time presents the beneficial effects of KIR genotype matching in reducing cGVHD in myeloablative transplant setting using HLA matched (sibling and unrelated) donors. The findings offer a clinically applicable donor selection strategy that can help control cGVHD without affecting the risk of relapse and/or identify patients at a high risk of developing cGVHD as potential candidates for preemptive therapy. The findings also affirm the beneficial effect of one or more missing inhibitory KIR ligands in the recipient in reducing relapse and improving a relapse free survival in unrelated donor transplants.
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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) | 0.002 | 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".