Abstract A016: Reprogramming of immune-enhancing neutrophils by subclinical low-dose endotoxin for the treatment of cancer
Bibliographic record
Abstract
Abstract Despite the recent resurgence of the pioneering concept of “Coley’s toxin” in ushering in anti-cancer immune therapies highlight by check-point inhibitors and CAR-T approaches, fundamental mechanisms responsible for the immune-enhancing efficacy of innate low-dose “Coley’s toxin” remain poorly understood. This study aims to reveal the novel reprogramming of immune-enhancing neutrophils by super-low dose endotoxin conducive for innate immunity-based anti-cancer therapies. We perform scRNAseq analyses and reveal that neutrophils trained by super-low dose endotoxin (SL-LPS) adopt a unique immune-enhancing cluster characterized by CD177loCD11BloCD80hiCD40hi. Transfusion of SL-LPS trained neutrophils into recipient mice with AOM/DSS-inducing colorectal tumors exhibit potent efficacy in reducing tumor burden. SL-LPS trained neutrophils show relieved suppression of adaptive T cells as compared to un-trained neutrophils in vivo and in vitro. Mechanistically, SL-LPS enables the generation of immune-enhancing neutrophils through activating STAT5 and reducing innate suppressor IRAK-M. Herein, we present evidence revealing the key principle that underlying the immune-enhancing effects of super-low dose endotoxin, unrevealing the long-held mystery of low-dose “Coley’s toxin” in generically boosting host anti-tumor defense. Our mechanistic and translational observations not only reveal fundamental mechanisms for re-programming immune-enhancing neutrophils, but also provide a proof-of-principle in developing innate neutrophil-based anti-tumor therapeutics. Citation Format: Yao Zhang, Liwu Li. Reprogramming of immune-enhancing neutrophils by subclinical low-dose endotoxin for the treatment of cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A016.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".