CD47 blockade augmentation of trastuzumab antitumor efficacy dependent on antibody-dependent cellular phagocytosis
Bibliographic record
Abstract
The HER2-specific monoclonal antibody (mAb), trastuzumab, has been the mainstay of therapy for HER2+ breast cancer (BC) for approximately 20 years. However, its therapeutic mechanism of action (MOA) remains unclear, with antitumor responses to trastuzumab remaining heterogeneous and metastatic HER2+ BC remaining incurable. Consequently, understanding its MOA could enable rational strategies to enhance its efficacy. Using both murine and human versions of trastuzumab, we found its antitumor activity dependent on Fcγ receptor stimulation of tumor-associated macrophages (TAMs) and antibody-dependent cellular phagocytosis (ADCP), but not cellular cytotoxicity (ADCC). Trastuzumab also stimulated TAM activation and expansion, but did not require adaptive immunity, natural killer cells, and/or neutrophils. Moreover, inhibition of the innate immune ADCP checkpoint, CD47, significantly enhanced trastuzumab-mediated ADCP and TAM expansion and activation, resulting in the emergence of a unique hyperphagocytic macrophage population, improved antitumor responses, and prolonged survival. In addition, we found that tumor-associated CD47 expression was inversely associated with survival in HER2+ BC patients and that human HER2+ BC xenografts treated with trastuzumab plus CD47 inhibition underwent complete tumor regression. Collectively, our study identifies trastuzumab-mediated ADCP as an important antitumor MOA that may be clinically enabled by CD47 blockade to augment therapeutic 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 | 0.000 | 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".