SIRPαFc, a CD47-Blocking Cancer Immunotherapeutic, Sensitizes Malignant B Cells to Macrophage-Mediated Destruction
Bibliographic record
Abstract
Abstract Macrophages commonly infiltrate tumor microenvironments and can phagocytose and destroy malignant cells. Cancer cells, however, can inhibit the tumoricidal activity of macrophages by expressing CD47 on their surface. CD47 delivers an anti-phagocytic ("do not eat") signal by binding signal-regulatory protein α (SIRPα) on the surface of macrophages. There is strong evidence that many liquid and solid tumors exploit the CD47-SIRPα pathway to escape macrophage-mediated destruction. Blockade of this inhibitory axis using a soluble SIRPα-Fc fusion protein (SIRPαFc) has emerged as a promising strategy to neutralize the suppressive effects of CD47 and promote the eradication of tumor cells. Here we have examined the effect of SIRPαFc on malignant human B cells in vitro and in vivo. We first assessed the binding of SIRPαFc to a panel of established cell lines and primary cells from patients with diffuse large B cell lymphoma, Burkitt's lymphoma, multiple myeloma and acute lymphoblastic leukemia. SIRPαFc exhibited strong, dose-dependent binding to all tumor cells, with an average effective half-maximal concentration of approximately 150 nM. Next, the ability of SIRPαFc to promote macrophage-mediated phagocytosis of human tumor cells was examined using confocal microscopy. In cultures left untreated or treated with a control Fc fragment, macrophages exhibited a low level of phagocytosis, consistent with CD47-mediated suppression. Blockade of CD47 on the target cells using SIRPαFc dramatically increased macrophage phagocytosis of tumor cells. The majority of established cell lines and all primary human tumors were sensitized to macrophage-mediated destruction, including both peripheral blood- and bone marrow-derived primary tumor samples. Finally, we assessed the in vivo activity of SIRPαFc in CD20hi (Raji) and CD20low (Namalwa) B lymphoma xenograft models. SIRPαFc treatment significantly reduced Raji growth and increased host mouse survival (time to euthanasia), and completely ablated the growth of Namalwa tumors, the latter being insensitive to rituximab therapy. In conclusion, SIRPαFc demonstrated in vitro activity against a broad range of human B cell tumors and was highly effective at controlling the growth of aggressive B lymphoma xenografts in mice, including a CD20low tumor that was non-responsive to rituximab. These data support the evaluation of SIRPαFc in patients with B cell malignancies. Disclosures Nielsen Viller: Trillium Therapeutics Inc.: Employment. Vareki:Trillium Therapeutics Inc.: Research Funding. Dodge:Trillium Therapeutics Inc.: Employment. Chen:Trillium Therapeutics Inc.: Employment. Lee:Trillium Therapeutics Inc.: Employment. Chai:Trillium Therapeutics Inc.: Employment. Pang:Trillium Therapeutics Inc.: Employment. Wong:Trillium Therapeutics Inc.: Employment. Trudel:Novartis: Honoraria; Oncoethix: Research Funding; BMS: Honoraria; Trillium Therapeutics Inc.: Research Funding; Celgene: Equity Ownership, Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau. Figueredo:Trillium Therapeutics Inc.: Research Funding. Pampillo:Trillium Therapeutics Inc.: Research Funding. Koropatnick:Trillium Therapeutics Inc.: Research Funding. Petrova:Trillium Therapeutics Inc.: Employment. Uger:Trillium Therapeutics Inc.: Employment.
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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".