Natural Killer Cells as a Potential Biomarker for Predicting Immunotherapy Efficacy in Patients with Non-Small Cell
Yong-Hee Cho1, Myeong Geun Choi2, Dong Ha Kim1
1Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, 05505, South Korea.
Background:
Immunotherapy with immune checkpoint inhibitors for non-small cell lung cancer (NSCLC) has emerged as an important treatment option. Although immunotherapy may significantly improve survival and quality of life, response rates are as low as 20% in NSCLC patients.
Objective:
The identification of reliable biomarkers predicting response to immunotherapy is required urgently to determine patient selection guidelines.
Patients And Methods:
Peripheral blood mononuclear cells (PBMCs) from nine NSCLC patients were collected pre- and post-treatment with immunotherapy. The immune cell composition of PBMCs was analyzed using CyTOF with an optimized 32-marker panel. The natural killer (NK) cell activity was assessed with the measurement of interferon (INF)-γ using an NK Vue™ kit.
Results:
We found that the percentages of NK cell populations in the immune cells of PBMCs were prominently elevated in the immunotherapy responder group when compared with non-responders. While no meaningful differences were observed in other populations of immune cells, consistent with these results, the overall activity of NK cells in responders was highly elevated compared with that of non-responders. From the analysis of NK subsets, although differences in the population of early NK cells were not observed, the functionally differentiated late NK cells were prominently high in responders.
Conclusions:
The overall activity or number of NK cells may be a useful biomarker to predict immunotherapy response in patients with NSCLC.
Insights
Natural killer (NK) cell activity and numbers in peripheral blood mononuclear cells (PBMCs) can predict immunotherapy response in non-small cell lung cancer (NSCLC) patients, offering a potential biomarker for treatment selection.
Area of Science:
- Immunology
- Oncology
- Biomarker Discovery
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