Abstract 2799: Targeting CXCR2-mediated neutrophil recruitment to lung cancer
Bibliographic record
Abstract
Abstract With recent advances in immunotherapy, it is evident that targeting the tumor microenvironment (TME) is an effective strategy to treat lung cancer (LC), however, more than half LC patients are still resistant to therapy. Limited attention was given to the relevance of the innate immune system despite its critical role in triggering adaptive responses. Neutrophils (PMNs) are the predominant circulating leukocyte in humans. PMNs are associated with developing lesions and are the main immune component of primary non-small cell LC (NSCLC). Multiple studies support the notion that PMNs promote tumor progression, however, the exact mechanisms in which these PMNs are recruited to the primary and metastatic lung TMEs remain unclear. To this end, we examined available genomic databases of > 1,000 NSCLC primary adenocarcinoma (ADC) patients and observed that high expression of all CXCR2 ligands (CXCL1-8 and MIF) correlate with poor survival in lung ADC. Lung ADC patients display one of the highest fold increases of these ligands as compared to all other cancers. We then performed shRNA knock down (KD) of CXCL1 and MIF in A549 and tested the migration of PMNs towards treated and control cell lines using the novel microfluidic device. We observe 3-fold increase of PMN migration towards A549 compared to control. This increase was significantly inhibited in MIF and CXCL1 KDs as well as using MIF and CXCL1 neutralizing antibodies (NA) as compared to controls. PMN migration was higher to A549 then to PC9EN, and treatment of PMNs with a CXCR2 NA led to a decrease in their migration to A549 while unaffecting their migration to PC9EN. Due to the lack of similar genomic databases on LC metastasis, we profiled liver homogenates of mice intrasplenically injected with liver-metastatic Lewis lung carcinoma (LLC) and observed that Cxcl1 was the most overexpressed gene as compared to non-tumor bearing mice (non-TBM). We then KD CXCL1 from the liver metastatic LLC cell line and compared its capacity to recruit PMNs in live mice using intravital microscopy. We observe a decrease in the number of PMNs around developing CXCL1 KD LLC tumors compared to control LLC. We also observe a decrease in PMN migration toward the CXCL1 KD LLC tumors as compared the control LLC. This resulted in a significant decrease in liver metastasis of the CXCL1 KD LLC as compared to control LLC injected mice. Altogether, our data highlight the importance of CXCR2-mediated PMN migration in primary LC and the establishment of liver metastasis from LC. Thus, inhibiting CXCR2 represents a promising strategy to impede primary tumor growth and metastatic dissemination of LC. Citation Format: Roni F. Rayes, Jack G. Mouhanna, Claire Wang, Simon Milette, Carson Wong, Mariana Usatii, Betty Giannias, France Bourdeau, Rachel Mot, Arvind Chandrasekaran, Christopher Moraes, Sidong Huang, Daniela Quail, Logan Walsh, Veena Sangwan, Nicholas Bertos, Pierre-Olivier Fiset, Jonathan Cools-Lartigue, Lorenzo E. Ferri, Jonathan D. Spicer. Targeting CXCR2-mediated neutrophil recruitment to lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2799.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".