Potential Interventions in Sepsis-Related Acute Kidney Injury
Bibliographic record
Abstract
Background Neutrophils kill antibody-opsonized tumor cells using trogocytosis, a unique mechanism of destruction of the target plasma. This previously unknown cytotoxic process of neutrophils is dependent on antibody opsonization, Fcγ receptors and CD11b/CD18 integrins. Here, we demonstrate that tumor cells can escape neutrophil-mediated cytotoxicity by calcium (Ca2+)-dependent and exocyst complex-dependent plasma membrane repair. Methods We knocked down EXOC7 or EXOC4, two exocyst components, to evaluate their involvement in tumor cell membrane repair after neutrophil-induced trogocytosis. We used live cell microscopy and flow cytometry for visualization of the host and tumor cell interaction and tumor cell membrane repair. Last, we reported the mRNA levels of exocyst in breast cancer tumors in correlation to the response in trastuzumab-treated patients. Results We found that tumor cells can evade neutrophil antibody-dependent cellular cytotoxicity (ADCC) by Ca2+-dependent cell membrane repair, a process induced upon neutrophil trogocytosis. Absence of exocyst components EXOC7 or EXOC4 rendered tumor cells vulnerable to neutrophil-mediated ADCC (but not natural killer cell-mediated killing), while neutrophil trogocytosis remained unaltered. Finally, mRNA levels of exocyst components in trastuzumab-treated patients were inversely correlated to complete response to therapy. Conclusions Our results support that neutrophil attack towards antibody-opsonized cancer cells by trogocytosis induces an active repair process by the exocyst complex in vitro. Our findings provide insight to the possible contribution of neutrophils in current antibody therapies and the tolerance mechanism of tumor cells and support further studies for potential use of the exocyst components as clinical biomarkers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".