IgA-Mediated Killing of Tumor Cells by Neutrophils Is Enhanced by CD47–SIRPα Checkpoint Inhibition
Bibliographic record
Abstract
Abstract Therapeutic monoclonal antibodies (mAb), directed toward either tumor antigens or inhibitory checkpoints on immune cells, are effective in cancer therapy. Increasing evidence suggests that the therapeutic efficacy of these tumor antigen–targeting mAbs is mediated—at least partially—by myeloid effector cells, which are controlled by the innate immune-checkpoint interaction between CD47 and SIRPα. We and others have previously demonstrated that inhibiting CD47–SIRPα interactions can substantially potentiate antibody-dependent cellular phagocytosis and cytotoxicity of tumor cells by IgG antibodies both in vivo and in vitro. IgA antibodies are superior in killing cancer cells by neutrophils compared with IgG antibodies with the same variable regions, but the impact of CD47–SIRPα on IgA-mediated killing has not been investigated. Here, we show that checkpoint inhibition of CD47–SIRPα interactions further enhances destruction of IgA antibody–opsonized cancer cells by human neutrophils. This was shown for multiple tumor types and IgA antibodies against different antigens, i.e., HER2/neu and EGFR. Consequently, combining IgA antibodies against HER2/neu or EGFR with SIRPα inhibition proved to be effective in eradicating cancer cells in vivo. In a syngeneic in vivo model, the eradication of cancer cells was predominantly mediated by granulocytes, which were actively recruited to the tumor site by SIRPα blockade. We conclude that IgA-mediated tumor cell destruction can be further enhanced by CD47–SIRPα checkpoint inhibition. These findings provide a basis for targeting CD47–SIRPα interactions in combination with IgA therapeutic antibodies to improve their potential clinical efficacy in tumor patients.
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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 | 0.000 |
| 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".