Macrophages eliminate circulating tumor cells after monoclonal antibody therapy
Bibliographic record
Abstract
The use of monoclonal antibodies (mAbs) as therapeutic tools has increased dramatically in the last decade and is now one of the mainstream strategies to treat cancer. Nonetheless, it is still not completely understood how mAbs mediate tumor cell elimination or the effector cells that are involved. Using intravital microscopy, we found that antibody-dependent phagocytosis (ADPh) by macrophages is a prominent mechanism for removal of tumor cells from the circulation in a murine tumor cell opsonization model. Tumor cells were rapidly recognized and arrested by liver macrophages (Kupffer cells). In the absence of mAbs, Kupffer cells sampled tumor cells; however, this sampling was not sufficient for elimination. By contrast, antitumor mAb treatment resulted in rapid phagocytosis of tumor cells by Kupffer cells that was dependent on the high-affinity IgG-binding Fc receptor (FcγRI) and the low-affinity IgG-binding Fc receptor (FcγRIV). Uptake and intracellular degradation were independent of reactive oxygen or nitrogen species production. Importantly, ADPh prevented the development of liver metastases. Tumor cell capture and therapeutic efficacy were lost after Kupffer cell depletion. Our data indicate that macrophages play a prominent role in mAb-mediated eradication of tumor cells. These findings may help to optimize mAb therapeutic strategies for patients with cancer by helping us to aim to enhance macrophage recruitment and activity.
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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) | 0.001 | 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".