Abstract 1099: Molecular drivers of neutrophil recruitment to primary non-small cell lung cancer
Bibliographic record
Abstract
Abstract Neutrophils are associated with developing cancer lesions and are the main immune component of primary non-small cell lung cancer (NSCLC). Multiple studies support the notion that tumor associated neutrophils (TANs) can promote tumor progression. We hypothesize that there is a hierarchy of molecular cues produced by developing lung cancers that guide circulating neutrophils to infiltrate the tumor microenvironment and become TANs. Identifying these cues may permit modulation of neutrophil infiltration within developing lung cancers and may thereby act as an immunotherapeutic tool to suppress cancer progression and improve response to existing therapeutics. To this end, we profiled 5 established NSCLC cell lines representing the common NSCLC subtypes using a qRT-PCR 84 gene panel (A549; KRAS mutant, PC9; EGFR mutant, H1993; MET amplification, H3122; EML4-ALK translocation, HCC78; ROS1 translocation). We focused on 4 of the most commonly upregulated genes in all cell lines, which were osteopontin (Spp1), vascular endothelial growth factor A (VEGF-A), macrophage inhibitory factor (MIF), and C-X-C motif ligand 1 (CXCL1). After confirming protein expression of these targets by western blot, we performed shRNA knock down (KD) of these genes and tested the migration of neutrophils towards treated and control cell lines in a novel microfluidic device that allows increased throughput studies of neutrophil attractants. Findings from KD experiments were confirmed via antibody-mediated inhibition. We observed a 3-fold increase of neutrophil migration to the A549 cancer cell line compared to the serum free control (p=0.0265). Furthermore, this increase was inhibited in Spp1 (64% decrease), MIF (84%), VEGF (82%) KDs and their corresponding neutralizing antibodies. We have therefore identified 4 proteins that play a key role in neutrophil recruitment to NSCLC cell lines in vitro and have demonstrated the application of a simple microfluidic device to test neutrophil migration patterns. This data provides the basis for in vivo investigations to elucidate the key molecular cues for neutrophil infiltration within developing lung cancers. Citation Format: Claire Wang, Roni Rayes, Jack Mouhanna, Betty Giannias, Arvind Chandrasekaran, Rachel Mot, Christopher Moraes, Sidong Huang, Jonathan Cools-Lartigue, Nicholas Bertos, Lorenzo Ferri, Jonathan Spicer. Molecular drivers of neutrophil recruitment to primary non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1099.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".