Exploitable mechanisms of antibody and CAR mediated macrophage cytotoxicity
Bibliographic record
Abstract
Macrophages infiltrate solid tumors and either support survival or induce cancer cell death through phagocytosis or cytotoxicity. To uncover regulators of macrophage cytotoxicity towards cancer cells, we perform two co-culture CRISPR screens using CAR-macrophages targeting different tumor associated antigens. Both identify ATG9A as an important regulator of this cytotoxic activity. In vitro and in vivo, ATG9A depletion in cancer cells sensitizes them to macrophage-mediated killing. Proteomic and lipidomic analyses reveal that ATG9A deficiency impairs the cancer cell response to macrophage-induced plasma membrane damage through defective lysosomal exocytosis, reduced ceramide production, and disrupted caveolar endocytosis. Depleting non-cytotoxic macrophages using CSF1R inhibition while preventing ATG9A-mediated tumor membrane repair enhances the anti-tumor activity of therapeutic antibodies in mice. Thus, macrophage cytotoxicity plays an important role in tumor elimination during antibody or CAR-macrophage treatment, and inhibiting tumor membrane repair via ATG9A, particularly in combination with cytotoxic macrophage enrichment through CSF1R inhibition, improves tumor-targeting macrophage efficacy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".