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External validation of models for KIR2DS1/KIR3DL1-informed selection of hematopoietic cell donors fails.

ABSTRACT: Several studies suggest that harnessing natural killer (NK) cell reactivity mediated through killer cell immunoglobulin-like receptors (KIRs) could reduce the risk of relapse after allogeneic hematopoietic cell transplantation. Based on one promising model, information on KIR2DS1 and KIR3DL1 and their cognate ligands can be used to classify donors as KIR-advantageous or KIR-disadvantageous. This study was aimed at externally validating this model in unrelated donor hematopoietic cell transplantation. The impact of the predictor on overall survival (OS) and relapse incidence was tested in a Cox regression model adjusted for patient age, a modified disease risk index, Karnofsky performance status, donor age, HLA match, sex match, cytomegalovirus match, conditioning intensity, type of T-cell depletion, and graft type. Data from 2222 patients with acute myeloid leukemia or myelodysplastic syndrome were analyzed. KIR genes were typed by using high-resolution amplicon-based next-generation sequencing. In univariable analyses and subgroup analyses, OS and the cumulative incidence of relapse of patients with a KIR-advantageous donor were comparable to patients with a KIR-disadvantageous donor. The adjusted hazard ratio from the multivariable Cox regression model was 0.99 (Wald test, P = .93) for OS and 1.04 (Wald test, P = .78) for relapse incidence. We also tested the impact of activating donor KIR2DS1 and inhibition by KIR3DL1 separately but found no significant impact on OS and the risk of relapse. Thus, our study shows that the proposed model does not universally predict NK-mediated disease control. Deeper knowledge of NK-mediated alloreactivity is necessary to predict its contribution to graft-versus-leukemia reactions and to eventually use KIR genotype information for donor selection.

SUBMITTER: Schetelig J

PROVIDER: S-EPMC7162689 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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External validation of models for KIR2DS1/KIR3DL1-informed selection of hematopoietic cell donors fails.

Schetelig Johannes J Baldauf Henning H Heidenreich Falk F Massalski Carolin C Frank Sandra S Sauter Jürgen J Stelljes Matthias M Ayuk Francis Ayuketang FA Bethge Wolfgang A WA Bug Gesine G Klein Stefan S Wendler Sarah S Lange Vinzenz V de Wreede Liesbeth C LC Fürst Daniel D Kobbe Guido G Ottinger Hellmut D HD Beelen Dietrich W DW Mytilineos Joannis J Fleischhauer Katharina K Schmidt Alexander H AH Bornhäuser Martin M

Blood 20200401 16

Several studies suggest that harnessing natural killer (NK) cell reactivity mediated through killer cell immunoglobulin-like receptors (KIRs) could reduce the risk of relapse after allogeneic hematopoietic cell transplantation. Based on one promising model, information on KIR2DS1 and KIR3DL1 and their cognate ligands can be used to classify donors as KIR-advantageous or KIR-disadvantageous. This study was aimed at externally validating this model in unrelated donor hematopoietic cell transplanta ...[more]

PMID: 31932846

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Project description:Results from registry studies suggest that harnessing Natural Killer (NK) cell reactivity mediated through Killer cell Immunoglobulin-like Receptors (KIR) could reduce the risk of relapse after allogeneic Hematopoietic Cell Transplantation (HCT). Several competing models have been developed to classify donors as KIR-advantageous or disadvantageous. Basically, these models differ by grouping donors based on distinct KIR-KIR-ligand combinations or by haplotype motif assignment. This study aimed to validate different models for unrelated donor selection for patients with Myelodysplatic Syndromes (MDS) or secondary Acute Myeloid Leukemia (sAML). In a joint retrospective study of the European Society for Blood and Marrow Transplantation (EBMT) and the Center for International Blood and Marrow Transplant Research (CIBMTR) registry data from 1704 patients with secondary AML or MDS were analysed. The cohort consisted mainly of older patients (median age 61 years) with high risk disease who had received chemotherapy-based reduced intensity conditioning and anti-thymocyte globulin prior to allogeneic HCT from well-matched unrelated stem cell donors. The impact of the predictors on Overall Survival (OS) and relapse incidence was tested in Cox regression models adjusted for patient age, a modified disease risk index, performance status, donor age, HLA-match, sex-match, CMV-match, conditioning intensity, type of T-cell depletion and graft type. KIR genes were typed using high-resolution amplicon-based next generation sequencing. In univariable and multivariable analyses none of the models predicted OS and the risk of relapse consistently. Our results do not support the hypothesis that optimizing NK-mediated alloreactivity is possible by KIR-genotype informed selection of HLA-matched unrelated donors. However, in the context of allogeneic transplantation, NK-cell biology is complex and only partly understood. KIR-genes are highly diverse and current assignment of haplotype motifs based on the presence or absence of selected KIR genes is over-simplistic. As a consequence, further research is highly warranted and should integrate cutting edge knowledge on KIR genetics, and NK-cell biology into future studies focused on homogeneous groups of patients and treatment modalities.

Project description:Background and purposeThe net benefit of carotid endarterectomy (CEA) is determined partly by the risk of procedural stroke or death. Current guidelines recommend CEA if 30-day risks are <6% for symptomatic stenosis and <3% for asymptomatic stenosis. We aimed to identify prediction models for procedural stroke or death after CEA and to externally validate these models in a large registry of patients from the United States.MethodsWe conducted a systematic search in MEDLINE and EMBASE for prediction models of procedural outcomes after CEA. We validated these models with data from patients who underwent CEA in the American College of Surgeons National Surgical Quality Improvement Program (2011-2017). We assessed discrimination using C statistics and calibration graphically. We determined the number of patients with predicted risks that exceeded recommended thresholds of procedural risks to perform CEA.ResultsAfter screening 788 reports, 15 studies describing 17 prediction models were included. Nine were developed in populations including both asymptomatic and symptomatic patients, 2 in symptomatic and 5 in asymptomatic populations. In the external validation cohort of 26 293 patients who underwent CEA, 702 (2.7%) developed a stroke or died within 30-days. C statistics varied between 0.52 and 0.64 using all patients, between 0.51 and 0.59 using symptomatic patients, and between 0.49 to 0.58 using asymptomatic patients. The Ontario Carotid Endarterectomy Registry model that included symptomatic status, diabetes, heart failure, and contralateral occlusion as predictors, had C statistic of 0.64 and the best concordance between predicted and observed risks. This model identified 4.5% of symptomatic and 2.1% of asymptomatic patients with procedural risks that exceeded recommended thresholds.ConclusionsOf the 17 externally validated prediction models, the Ontario Carotid Endarterectomy Registry risk model had most reliable predictions of procedural stroke or death after CEA and can inform patients about procedural hazards and help focus CEA toward patients who would benefit most from it.

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Project description:IntroductionExternal validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting.MethodsWe compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1) the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2) the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury.ResultsThe permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples) and heterogeneous in scenario 2 (in 17%-39% of simulated samples). Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2.ConclusionThe recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients.

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Dataset Information

External validation of models for KIR2DS1/KIR3DL1-informed selection of hematopoietic cell donors fails.

Publications

External validation of models for KIR2DS1/KIR3DL1-informed selection of hematopoietic cell donors fails.

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