Abstract 2863: Cancer extracellular vesicles induce lymph node metastasis via neutrophil extracellular traps
Bibliographic record
Abstract
Abstract In most human cancers, regional lymph nodes (LNs) are the first sites of metastasis. In addition to being an important part of the tumor staging system, with the advent of novel therapies, lymph node metastasis has become a crucial clinical intervention point before distant metastasis, the leading cause of cancer-associated deaths. To initiate metastasis, the conditions of LNs need to be optimized for tumor cell deposition and growth. This process is believed to be mediated by the activation of immune cells including polymorphonuclear neutrophils (PMNs), and tumor derived factors, such as extracellular vesicles (EVs). Indeed, tumor derived EVs (tEVs) were shown to prepare sentinel LNs for increased melanoma metastasis, however, the cellular mechanism is not well defined. Early observations suggest that PMNs and neutrophil extracellular traps (NETs), DNA comprising structures that are extruded in response to inflammatory cues, are associated with adverse oncologic outcomes. Moreover, PMNs exhibit considerable plasticity to tEVs, as gastric tEVs can polarize PMN toward a pro-tumor (N2) phenotype and induce NET formation. Thus, one potential mechanism of increased LN metastasis is that tEVs recruit PMNs and propend NETs formation. Here, we show that lymphatic PMN accumulation is associated with higher rates of LN metastasis in human esophageal cancer patients. Furthermore, we demonstrate that LN PMN accumulation is mediated by tEVs-lymphatic interaction both in vitro and in vivo. Finally, we demonstrate that lymphatic PMN facilitate metastasis through the accumulation of PMN prior to tumor ingress. Using Boyden chamber assays, we observed an increase in PMN migration towards tEVs educated lymphatic endothelial cells (LECs). Moreover, ELISA showed tEVs educated LEC increased secretion of the PMN chemoattractants CXCL4 and CXCL8. Additionally, through confocal microscopy and immunofluorescence, we observed that tEVs induced PMN recruitment to LNs and NETs released in vivo in a dose-dependent manner. Finally, using transgenic pad4-/- knockout mice, which are unable to generate NETs, we showed that the absence of NETs led to decreased LN metastasis. Together, these findings highlight the role of tumor derived tEVs both as PMN recruiters to LNs and NETs inducers. By further investigating the detailed mechanism and the efficiency of NETs targeting agents, this project will lead to major advances in the management of cancer patients. Citation Format: Xin SU, Ariane Brassard, Ramin Rohanizadeh, Iqraa Dhoparee-Doomah, Betty Giannias, France Bourdeau, Veena Sangwan, Roni F. Rayes, Jonathan D. Spicer, Lorenzo E. Ferri, Jonathan J. Cools-Lartigue. Cancer extracellular vesicles induce lymph node metastasis via neutrophil extracellular traps [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2863.
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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".