A practical difference from the normal approach to evaluate natural killer (NK) cells and NK cell neoplasms by flow cytometry

Abstract Natural killer (NK) cell neoplasms span indolent to highly aggressive entities, and flow cytometric immunophenotyping is central to their detection and classification. However, distinguishing reactive from neoplastic NK cell expansions can be challenging because NK cells lack somatically recombined antigen receptors, and reactive states may produce restricted phenotypes that mimic clonality. This best‐practices manuscript outlines a practical, flow cytometry‐based approach to NK cell analysis that integrates (i) robust lineage definition and gating, (ii) subset‐aware interpretation of CD16/CD56 patterns, (iii) inference of clonality using killer cell immunoglobulin‐like receptor (KIR; CD158) repertoire restriction and other NK cell receptors. We highlight key pitfalls—particularly the limited utility of KIR‐based clonality inference in CD56 bright CD16 − NK cells and the potential for adaptive NK cell expansions to appear KIR‐restricted—and propose an operational reflex algorithm triggered by NK cell expansion and/or defined immunophenotypic abnormalities in screening tubes. Case‐based examples illustrate common scenarios, including NK‐large granular lymphocytic leukemia, extranodal NK/T‐cell lymphoma with circulating CD56 bright CD16 − cells, reactive adaptive NK cell expansions, and concurrent neoplastic and reactive NK clones. This framework supports standardized, reproducible NK cell assessment and improves specificity of flow‐based clonality inference in routine clinical practice.

Authors

Institutions

Publication Details

Journal
Cytometry Part B Clinical Cytometry
Published
2026-09-25
DOI
https://doi.org/10.1002/cyto.b.70076
Primary Topic
Immune Cell Function and Interaction
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

A practical difference from the normal approach to evaluate natural killer (NK) cells and NK cell neoplasms by flow cytometry

Afshin Shameli, Jonathan R. Fromm, Erik Ames, Bartosz J. Grzywacz et al.
Cytometry Part B Clinical Cytometry
Immune Cell Function and Interaction
article

A practical difference from the normal approach to evaluate natural killer (NK) cells and NK cell neoplasms by flow cytometry

Afshin Shameli, Jonathan R. Fromm, Erik Ames, Bartosz J. Grzywacz, Aaron J. Wilk, Sindhu Cherian, Jean S. Oak, Min Shi
article en

Abstract

Abstract Natural killer (NK) cell neoplasms span indolent to highly aggressive entities, and flow cytometric immunophenotyping is central to their detection and classification. However, distinguishing reactive from neoplastic NK cell expansions can be challenging because NK cells lack somatically recombined antigen receptors, and reactive states may produce restricted phenotypes that mimic clonality. This best‐practices manuscript outlines a practical, flow cytometry‐based approach to NK cell analysis that integrates (i) robust lineage definition and gating, (ii) subset‐aware interpretation of CD16/CD56 patterns, (iii) inference of clonality using killer cell immunoglobulin‐like receptor (KIR; CD158) repertoire restriction and other NK cell receptors. We highlight key pitfalls—particularly the limited utility of KIR‐based clonality inference in CD56 bright CD16 − NK cells and the potential for adaptive NK cell expansions to appear KIR‐restricted—and propose an operational reflex algorithm triggered by NK cell expansion and/or defined immunophenotypic abnormalities in screening tubes. Case‐based examples illustrate common scenarios, including NK‐large granular lymphocytic leukemia, extranodal NK/T‐cell lymphoma with circulating CD56 bright CD16 − cells, reactive adaptive NK cell expansions, and concurrent neoplastic and reactive NK clones. This framework supports standardized, reproducible NK cell assessment and improves specificity of flow‐based clonality inference in routine clinical practice.

Cytometry Part B Clinical Cytometry
Beth Israel Deaconess Medical Center (US), Mayo Clinic (US), University of Washington (US), Stanford University (US)
Openalex Percentile: Top 18%
Immune Cell Function and Interaction
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.