Hematopoietic Cell Transplantation Donor Selection Reimagined: KIR-KIR Ligand Interactions and a Formalized Donor Risk Index Effective at Predicting Survival
Bibliographic record
Abstract
Background When selecting a human leukocyte antigen (HLA) matched unrelated donor (URD) for hematopoietic cell transplantation (HCT) it is generally accepted that donor age, sex, ABO blood group and viral serologic status should be considered. However, the inter-relationship among these variables is not well established and a consensus on how strongly to consider each variable has not been reached. Selection of the optimal donor gets more complicated as new donor recipient pair (DRP) selection parameters, including killer immunoglobulin-like receptors (KIR) haplotypes are included. In this study we seek to develop a logic-based method to reduce the inconsistencies in donor selection in the HLA matched HCT. Methods VCU IRB approval was obtained for a retrospective review of eligible subjects who were adults with known KIR genotyping receiving HLA-A, B, C & DRB1 allelically matched URD HCT for hematologic malignancy between 2014 and 2017. Donor recipient pairs were selected based on donor age, sex match, CMV sero-status match, and ABO compatibility when possible; KIR genotype was not considered in DRP selection. KIR-KIR ligand interactions were calculated for each DR pair and interaction unit values were ascribed as follows; -1, when the donor possessed an inhibitory KIR (iKIR) and the recipient the corresponding HLA; +1, when the donor possessed iKIR and recipient lacked corresponding HLA (mKIR score, missing ligand). A novel inhibitory-missing KIR (IM-KIR) score was calculated for each HLA matched DRP by summing the interaction values as in equation 1. IM KIR Score = |iKIR| + |mKIRL| ………. [1] Univariate and multivariate analysis using Cox regression methods were utilized to evaluate donor parameters associated with overall survival. Weights of each donor risk variable (age, sex, CMV & ABO match) contribution were ascribed and summed up to determine donor risk parameter. Donor risk parameters and reciprocal-IM-KIR were finally combined into a donor risk index. Receiver operating characteristic curve- area under the curve (ROC-AUC) analysis was utilized to compare indices. Results Ninety-eight DRP with known HLA & KIR genotyping were studied. Median follow up at the time of analysis was 583 days. A higher IM-KIR score describes a DRP with increased iKIR-KIR ligand interactions and missing KIR ligand interactions; which was associated with a favorable survival after HCT, HR of 0.44 (95%CI: 0.26 to 0.73; P=0.002). Further analyses were performed using a reciprocal of this score. Univariate analysis of overall survival for donor age, sex match, ABO compatibility and CMV status were all statistically insignificant (p>0.05). However, the donor risk parameter was predictive of mortality with a hazard ratio (HR) of 2.76 (95% CI: 1.22-6.18, p=0.014). Covariate analysis of the donor risk parameter and reciprocal IM-KIR score were both predictive of survival independent of each other with HR 2.41 (1.05-5.54, p=0.038) and 2.35 (1.18-4.70, p=0.016) respectively. Combining the two into a donor risk index was predictive of survival with a HR of 2.38 (1.44-3.92, p=0.001). ROC-AUC comparison of survival for IM-KIR score and donor risk parameter showed statistically significant AUCs of 0.63 and 0.67 respectively. Further, the combined donor risk index shows improved sensitivity and specificity over the donor risk parameter with AUCs of 0.72 and 0.67 respectively. Conclusions A novel KIR-HLA interaction score, the IM-KIR score independently predicts survival in HLA matched DRP, as does a formalized donor risk parameter which includes non-HLA donor characteristics. Moreover, the addition of IM-KIR score to the donor risk parameter enhanced the specificity and sensitivity of predicting survival in these patients. If validated in a larger exploration and validation cohort this method of donor selection may improve the donor selection process, decreasing variability in clinical outcomes and improve overall survival. Disclosures No relevant conflicts of interest to declare.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".