Trastuzumab/pertuzumab combination therapy stimulates antitumor responses through complement-dependent cytotoxicity and phagocytosis
Bibliographic record
Abstract
Two HER2-specific mAbs, trastuzumab and pertuzumab (T+P), combined with chemotherapy comprise standard-of-care treatment for advanced HER2+ breast cancers (BC). While this antibody combination is highly effective, its synergistic mechanism-of-action (MOA) remains incompletely understood. Past studies have suggested that the synergy underlying this combination occurs through the different mechanisms elicited by these antibodies, with pertuzumab suppressing HER2 heterodimerization and trastuzumab inducing antitumor immunity. However, in vivo evidence for this synergy is lacking. In this study, we found that the therapeutic efficacy elicited by their combination occurs through their joint ability to activate the classical complement pathway, resulting in both complement-dependent cytotoxicity and complement-dependent cellular phagocytosis of HER2+ tumors. We also demonstrate that tumor C1q expression is positively associated with survival outcome in HER2+ BC patients and that complement regulators CD55 and CD59 were inversely correlated with outcome, suggesting the clinical importance of complement activity. Accordingly, inhibition of C1q in mice abolished the synergistic therapeutic activity of T+P therapy, whereas knockdown of CD55 and CD59 expression enhanced T+P efficacy. In summary, our study identifies classical complement activation as a significant antitumor MOA for T+P therapy that may be functionally enhanced to potentially augment clinical 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".