Natural Killer (NK) Cell Alloreactivity in Haploidentical Stem Cell Transplantation
Mar Luis-Hidalgo1, José Luis Piñana2, Carlos Solano2
1Centro de Transfusión de la Comunidad Valenciana, 46014 Valencia, Spain.
Cells
|July 25, 2025
Summary
This review explores natural killer (NK) cell roles in haploidentical stem cell transplants. It details KIR gene concepts and models predicting NK cell alloreactivity to improve donor selection.
Area of Science:
- Immunology
- Transplantation Biology
- Genetics
Background:
- Natural killer (NK) cells are crucial for immune surveillance and response.
- Haploidentical hematopoietic stem cell transplantation (HSCT) offers a viable treatment option when matched donors are unavailable.
- NK cell alloreactivity, influenced by KIR genes, plays a significant role in HSCT outcomes.
Purpose of the Study:
- To review the role of NK cells in haploidentical HSCT.
- To introduce theoretical concepts of KIR genes and their relevance to NK cell function.
- To define and compare NK alloreactivity prediction models used in donor selection for haploidentical HSCT.
Main Methods:
- Literature review of scientific articles and studies.
- Synthesis of theoretical concepts regarding KIR gene structure, polymorphism, and inheritance.
- Analysis of various NK alloreactivity prediction models (ligand-ligand, receptor-ligand, gene-gene, KIR haplotype, KIR-B donor group).
Main Results:
- KIR genes, their proteins, receptors, and HLA ligands are fundamental to NK cell alloreactivity.
- Several models exist to predict NK cell alloreactivity based on donor and recipient KIR gene and HLA ligand profiles.
- These models aim to predict potential NK cell-mediated rejection or graft-versus-host disease.
Conclusions:
- Understanding KIR gene interactions is key to predicting NK cell alloreactivity in haploidentical HSCT.
- The optimal model for predicting KIR alloreactivity and its clinical significance in donor selection remains an active area of research.
- Further investigation is needed to refine donor selection algorithms for improved haploidentical HSCT success rates.
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