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.
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
Usage data is cumulative from April 2024 through April 2025.
Usage | JCI | PMC |
---|---|---|
Text version | 924 | 262 |
88 | 83 | |
Figure | 218 | 5 |
Supplemental data | 47 | 5 |
Citation downloads | 81 | 0 |
Totals | 1,358 | 355 |
Total Views | 1,713 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.