TNF is a key cytokine mediating neutrophil cytotoxic activity in breast cancer patients
Bibliographic record
Abstract
Abstract We have previously shown a novel antimetastatic role for neutrophils in the premetastatic lung of mice in models of breast cancer. Here we expand on those findings in the context of human breast cancer. We assessed the cytotoxicity of neutrophils from 90 newly diagnosed breast cancer patients, 24 ductal carcinoma in situ patients, 56 metastatic breast cancer patients, and 64 women with no history of cancer. We report that neutrophils from metastatic and newly diagnosed breast cancer patients are significantly more cytotoxic than neutrophils from cancer-free individuals. We hypothesized that tumor-secreted factors ‘prime’ neutrophils to become cytotoxic. To identify these factors we assayed for cytokines in serum from 54 breast cancer patients and 35 cancer-free controls. Tumor necrosis factor (TNFα), MCP-1 (CCL2), and IL1RA significantly correlated with cytotoxicity and directly stimulated neutrophil cytotoxicity ex vivo. RNA-seq analyses found protein kinase C iota (PRKCI) to be over expressed in patient neutrophils relative to neutrophils from cancer-free individuals. PRKCI has been implicated in NADPH oxidase assembly, required for neutrophil-mediated cell cytotoxicity. Treatment of human neutrophils with TNF-induced PRKCI expression and cytotoxicity in samples that had low basal levels of PRKCI expression. To date, this work is the first to demonstrate the cytotoxic role of neutrophils in the peripheral blood of a large cohort of breast cancer patients, and that select cytokines appear to mediate the stimulation of neutrophil cytotoxicity. Further functional studies are necessary to identify clinically relevant means of stimulating neutrophil cytotoxicity as an effective barrier against disease progression and metastasis.
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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 | 0.001 |
| 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) | 0.001 | 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".