Splenic granulopoiesis and S100A9 drive resistance to checkpoint inhibitors conferred by liver metastases
Bibliographic record
Abstract
Abstract Here, we investigate why liver metastases reduce the efficacy of immune checkpoint inhibitors (CPI). The poor prognosis of patients with liver metastases is associated with a systemic increase in neutrophils. Using experimental models, we confirm that mice with liver metastases respond poorly to CPI, have elevated neutrophils and suppress the response of subcutaneous lesions to CPI. We demonstrate that liver metastases, acting partly via IL-6, boost granulopoiesis in the spleen and promote the generation of immature S100A9 hi neutrophils that suppress T-cell proliferation. Human liver metastases exhibit a similar increase in S100A9 hi neutrophils. Neutrophil depletion attenuates the growth of liver metastases, but not subcutaneous metastases. Moreover, genetic deletion of S100A9 enables liver metastases to be effectively treated with CPI, and prevents liver metastases from suppressing the response in subcutaneous metastases. Thus, we document how liver metastases specifically change splenic granulopoiesis leading to changes in the microenvironment of non-hepatic lesions, and how targeting a key neutrophil protein restores the efficacy of CPI.
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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) | 0.002 | 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 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".