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Donor-Recipient Matching For Killer Immunoglobulin-Like Receptor Genotypes Confers Protection Against Graft Versus Host Disease Without Affecting Disease Relapse After Allogeneic Hematopoietic Cell Transplantation

2013· article· en· W2558613112 on OpenAlexaffabout
Rehan M. Faridi, Taylor J. Kemp, Victor Lewis, Noureddine Berka, Jan Storek, Faisal Khan

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsFoothills Medical CentreCalgary Laboratory ServicesAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsTransplantationImmunologyHematopoietic stem cell transplantationHuman leukocyte antigenGraft-versus-host diseaseDiseaseGenotypeMedicineBiologyInternal medicineOncologyGeneAntigenGenetics

Abstract

fetched live from OpenAlex

Background Allogeneic hematopoietic cell transplantation (allo-HCT) is a curative therapy for malignant and non-malignant hematological disorders. Unfortunately, complications of HCT, primarily graft versus host disease (GVHD) and relapse of the underlying disease are substantial. In Alberta, all HCTs are performed with antithymocyte globulin conditioning and >95% of these use HLA matched donors. However, in spite of that approximately 35% Albertan HCT recipients die or suffer long-term from GVHD while another 20% die due to disease relapse. Unfortunately, strategies/treatments aiming to control GVHD in most cases lead to increase in the rate of disease relapse and infections. In the recent years, the natural killer (NK) cell genetic system has garnered substantial research interest as an immunogenetic system that significantly influences HCT outcomes. The complexity of NK cell function is modulated by a series of activating and inhibitory cell surface receptors known as the Killer Immunoglobulin-like Receptors (KIR). Here we set out to determine the influence of KIR matching between HCT donor and recipient pairs on allogenic HCT complications. Study design Hypothesizing that donor-recipient KIR gene/profile mismatch would affect the allo-HCT outcomes, we genotyped 92 (discovery cohort) and 111 (validation cohort) allo-HCT pairs by Luminex-based rSSO method. The KIR genotypes were classified into AA and Bx genotypes on the basis of KIR gene constitution of the individual. Donor (D) and recipients (R) were ‘matched' for KIR genotypes when both carried either AA (two copies of group A haplotypes) or B/x (at least one copy of group B haplotype) genotypes. Effect of KIR matching on significant GVHD (described as grade 2-4 acute GVHD or chronic GVHD needing systemic therapy) as well as disease relapse was analyzed using binomial regression and Kaplan-Meier statistics. Results As observed in both the discovery and validation cohorts, a significant protection against GVHD was conferred upon when both the donor and recipient were matched for the KIR genotypes (HR=2.224; p=0.01) without any effect on disease relapse (HR=1.098; p=0.934) (figure1). Conclusions In spite of D-R matching for major immunogenetic determinants like HLA, complications of allogeneic HCT are significant. Since GVHD represents a flip side to graft-vs-leukemia (GVL) reaction, strategies to decrease GVHD in most cases have resulted in increased relapse. Matching donor and recipients for KIR genotypes can offer a significant protection against GVHD without increasing the risk of disease relapse. Disclosures: No relevant conflicts of interest to declare.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.009
GPT teacher head0.212
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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