Abstract 1315: NETs act as immunosuppressive agents within lymph nodes during cancer
Bibliographic record
Abstract
Abstract Metastasis is responsible for 90% of all cancer-related deaths, making it the most significant challenge in cancer treatment for clinicians worldwide. Metastasis is thought to occur in a stepwise process, in which local lymph nodes (LNs) are first colonized by tumour cells before proceeding to distal organs. However, the mechanism by which LN metastasis facilitates distal metastasis is poorly understood. LNs undergo environmental changes to accommodate tumour cell growth within, notably by shifting the environment towards immunosuppression to shut down anti-tumour immune cells. This immunosuppressive environment is critical for the establishment of LN metastases, as cytotoxic CD8 T cells will otherwise neutralize incoming circulating tumour cells. Neutrophils are among the first cells recruited to tumour-draining LNs to mediate the environmental shift, and yet their method of action has not been fully elucidated. They have recently been found to secrete NETs within LNs during cancer, which are pro-inflammatory web-like formations of DNA decorated with antimicrobial peptides. While NETs have been shown to exert pro-tumour effects in the tumour microenvironment and to facilitate metastasis as a whole, their role in LNs has never been explored. Using a NETs deficient mouse model (PAD4-/-), we report that NETs deposition within the tumour-draining LNs of mice upregulates the Treg population while simultaneously downregulating anti-tumour CD8 T cell proliferation and activation. Following these findings, we performed an in vitro suppression assay and found that NETs-educated Tregs hinder the expansion of CD8 T cells more than non-NETs-educated Tregs. Finally, through an induced metastasis mouse model, we have observed that tumour-draining LN resection as well as the absence of NETs protects against the development of distal metastasis, and decreases body-wide inflammation as seen by a lowered Neutrophil-to-Lymphocyte ratio in the circulation. Taken together, these findings highlight the role of NETs deposition in LNs as a key player in disease progression and bring forward a potential target for anti-metastatic drug development. Citation Format: Ariane Brassard, Xin Su, Iqraa Dhoparee-Doomah, Sabrina Leo, Lixuan Feng, France Bourdeau, Betty Giannias, Corissa Larson, Qian Qiu, Jonathan Spicer, Lorenzo Ferri, Jonathan Cools-Lartigue. NETs act as immunosuppressive agents within lymph nodes during cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1315.
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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".