Abstract 1508: Primary tumors induce neutrophil extracellular traps with targetable metastasis promoting effects
Bibliographic record
Abstract
Abstract Targeting the dynamic tumor immune microenvironment can provide effective therapeutic strategies for cancer. Neutrophils (polymorphonuclear neutrophils, PMNs) are the predominant circulating leukocyte population in humans and are vital to fight infection. Despite mounting evidence that PMNs can promote tumor progression, depleting PMNs is not a viable therapeutic option. Neutrophil extracellular traps (NETs) are networks of extracellular neutrophil DNA fibers that are capable of trapping tumor cells and promoting their growth and their metastasis. Targeting NETs can therefore be a potentially successful therapeutic option to block the tumor promoting functions of PMNs. Here we demonstrate that circulating NET levels are elevated in esophageal, gastric and lung cancer patients compared to healthy controls. This increase correlates with disease stage and NET levels are independent predictors of advanced stage. Using pre-clinical murine models of lung and colon cancer, we observe elevated NET levels in tumor bearing mice compared to non-tumor bearing mice; these levels correlated with tumor size. NET levels significantly decrease following tumor resection or treatment with DNase1, a NET degrader, or neutrophil elastase inhibitor (NEi), a NET inhibitor. NET levels do not rise following tumor inoculation in peptidyl arginine deiminase-IV knock out (PAD4-/-) mice; PAD4 being an enzyme essential for citrullination of histones, a crucial step in NET release. Moreover, PMNs from tumor bearing mice are more primed for NETosis than PMNs from non-tumor bearing mice or NEi-treated or PAD4-/- tumor bearing mice. Finally, elevated in vivo hepatic adhesion and spontaneous liver and lung metastases are observed in tumor bearing mice compared to DNase1- or NEi-treated or PAD4-/- tumor bearing mice. Therefore, inhibiting NETs represents a promising strategy to impede metastatic dissemination in several types of cancer patients. Citation Format: Roni F. Rayes, Jack G. Mouhanna, Ioana Nicolau, Phil Vourtzoumis, Carson Wong, Jules Eustache, France Bourdeau, Betty Giannias, Aya Siblini, Emma Lee, Veena Sangwan, Simon Rousseau, Daniela Quail, Logan Walsh, Nicholas Bertos, Jonathan Cools-Lartigue, Lorenzo E. Ferri, Jonathan D. Spicer. Primary tumors induce neutrophil extracellular traps with targetable metastasis promoting effects [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 1508.
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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) | 0.005 | 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".