Anti-GD2 antibody disrupts GD2:Siglec-7 interactions and synergizes with CD47 blockade to mediate tumor eradication
Bibliographic record
Abstract
The disialoganglioside GD2 is consistently overexpressed in neuroblastoma and osteosarcoma, and is variably expressed in other sarcomas, gliomas, neuroendocrine tumors, and epithelial cancers. Anti-GD2 antibodies have improved the survival rates of patients with neuroblastoma only when administered as part of intense chemotherapy-based cytotoxic regimens, which are associated with debilitating late effects including hearing loss, growth retardation, and secondary leukemias. Despite broad expression of GD2 on osteosarcoma, anti-GD2 antibody has not mediated significant antitumor activity in that disease or any other GD2+ cancers. CD47 is a checkpoint molecule overexpressed on tumor cells that inhibits macrophage activity, and CD47 blockade has demonstrated promising clinical activity in early human trials. We investigated whether anti-CD47 antibody could enhance the efficacy of anti-GD2 antibody in neuroblastoma and other GD2+ malignancies. We demonstrate substantial synergy of these two agents, resulting in the recruitment of tumor associated macrophages (TAMs) to mediate robust and durable anti-tumor responses. The responses are driven by GD2-specific factors that reorient the balance of macrophage activity towards phagocytosis of tumor cells, including disruption of a newly described GD2:Siglec-7 axis. These results demonstrate the unique synergy of combining anti-GD2 with anti-CD47, which has the potential to significantly enhance outcomes for children with neuroblastoma and osteosarcoma and will soon be investigated in a first-in-human clinical trial.
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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 | 0.000 |
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".