Killer Immunoglobulin-Like Receptor-Ligand Interactions Predict Clinical Outcomes following Unrelated Donor Transplantations
Bibliographic record
Abstract
Killer immunoglobulin-like receptor (KIR) and KIR ligand (KIRL) interactions play an important role in natural killer (NK) cell-mediated graft-versus-leukemia effect following hematopoietic cell transplantation (HCT). However, there is considerable heterogeneity in the KIR gene and KIRL content in individuals, making it difficult to estimate the full clinical impact of NK cell reconstitution following HCT. Here we present a novel adaptive mathematical model designed to quantify these interactions to better assess the influence of NK cell-mediated alloreactivity on transplant outcomes. Ninety-eight HLA- matched unrelated donor (URD) HCT recipients were studied retrospectively. The KIR-KIRL interactions were quantified using a system of matrix equations. Unit values were ascribed to each KIR-KIRL interaction, and the directionality of interactions was denoted by either a positive (activating) or negative (inhibition) symbol; these interactions were then summed. The absolute values of both the missing KIRL and inhibitory KIR-KIRL interactions were significantly associated with overall survival and relapse. These score components were initially used to develop a weighted score (w-KIR score) and subsequently a simplified, nonweighted KIR-KIRL interaction score (IM-KIR score). Increased w-KIR score and IM-KIR score were predictive of all-cause mortality and relapse (w-KIR score: hazard ratio [HR], .37 [P = .001] and .44 [P = .044], respectively; IM-KIR score: HR, .5 [P = .049] and .44 [P = .002], respectively). IM-KIR score was also associated with NK cell reconstitution post-HCT. KIR-KIRL interactions as reflected by the w-KIR and IM-KIR scores influence both relapse risk and survival in recipients of HLA-matched URD HCT with hematologic malignancies.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".