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Neutrophil content predicts lymphocyte depletion and anti-PD1 treatment failure in NSCLC
Julia Kargl, … , Hamid Bolouri, A. McGarry Houghton
Julia Kargl, … , Hamid Bolouri, A. McGarry Houghton
Published December 19, 2019
Citation Information: JCI Insight. 2019;4(24):e130850. https://doi.org/10.1172/jci.insight.130850.
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Research Article Immunology Oncology

Neutrophil content predicts lymphocyte depletion and anti-PD1 treatment failure in NSCLC

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Abstract

Immune checkpoint inhibitor (ICI) treatment has recently become a first-line therapy for many non–small cell lung cancer (NSCLC) patients. Unfortunately, most NSCLC patients are refractory to ICI monotherapy, and initial attempts to address this issue with secondary therapeutics have proven unsuccessful. To identify entities precluding CD8+ T cell accumulation in this process, we performed unbiased analyses on flow cytometry, gene expression, and multiplexed immunohistochemical data from a NSCLC patient cohort. The results revealed the presence of a myeloid-rich subgroup, which was devoid of CD4+ and CD8+ T cells. Of all myeloid cell types assessed, neutrophils were the most highly associated with the myeloid phenotype. Additionally, the ratio of CD8+ T cells to neutrophils (CD8/PMN) within the tumor mass optimally distinguished between active and myeloid cases. This ratio was also capable of showing the separation of patients responsive to ICI therapy from those with stable or progressive disease in 2 independent cohorts. Tumor-bearing mice treated with a combination of anti-PD1 and SX-682 (CXCR1/2 inhibitor) displayed relocation of lymphocytes from the tumor periphery into a malignant tumor, which was associated with induction of IFN-γ–responsive genes. These results suggest that neutrophil antagonism may represent a viable secondary therapeutic strategy to enhance ICI treatment outcomes.

Authors

Julia Kargl, Xiaodong Zhu, Huajia Zhang, Grace H. Y. Yang, Travis J. Friesen, Melissa Shipley, Dean Y. Maeda, John A. Zebala, Jill McKay-Fleisch, Gavin Meredith, Afshin Mashadi-Hossein, Christina Baik, Robert H. Pierce, Mary W. Redman, Jeffrey C. Thompson, Steven M. Albelda, Hamid Bolouri, A. McGarry Houghton

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Figure 1

Unsupervised hierarchical clustering identifies immune response subtypes.

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Unsupervised hierarchical clustering identifies immune response subtypes...
(A) Unsupervised hierarchical clustering of n = 68 NSCLC tumor specimens using 51 distinct immune cell populations and subpopulations. Each population is expressed as relative abundance. (B) Heatmap of selected genes from Nanostring Immune Profiling gene expression panel (n = 58).

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