Blockade Of CD47 Using SIRPαFc: Role Of The Fc Region In Anti-Leukemic Activity and Tolerability
Bibliographic record
Abstract
Abstract Introduction CD47 binds to SIRPα on the surface of macrophages and delivers a “do not eat” signal that suppresses phagocytosis. There is increasing evidence that acute myeloid leukemia (AML) stem cells exploit the CD47-SIRPα pathway to escape macrophage-mediated destruction. Blockade of CD47 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 AML cells. However, little information is available regarding the optimal structure of SIRPαFc. In particular, the influence of the Fc region, which can mediate antibody-dependent cellular cytotoxicity and complement activation, on anti-leukemic activity and toxicity has not been explored. Results We have generated three unique human SIRPαFc fusion proteins that vary in their Fc regions: SIRPα-G1, which contains the Fc region from human IgG1 with full effector activity; SIRPα-G4, bearing the Fc region from human IgG4, which has low effector activity; and SIRPα-G4m, which possesses a mutated human IgG4 Fc region that is devoid of any effector activity. These three fusion proteins were tested for their ability to promote macrophage-mediated phagocytosis of patient-derived AML cells in vitro. Although all three proteins were able to stimulate tumor cell destruction, SIRPα-G4m was clearly the least potent, while SIRPα-G1 and SIRPα-G4 showed similar activity. Next, the anti-leukemic activity of the fusion proteins was assessed in an AML xenograft model in NOD.SCID mice. SIRPα-G1 induced a profound anti-leukemic effect and was superior to both SIRPα-G4 and SIRPα-G4m, particularly with respect to eradicating tumor cells within the transplanted femur. Thus, while only a low level of Fc activity was required for maximal pro-phagocytic activity in vitro, full effector activity (human IgG1) provided superior anti-leukemic activity in vivo. The strong anti-tumor activity of this fusion protein presumably results from the simultaneous delivery of a positive macrophage activating signal (through Fc receptors) and blockade of the negative “do not eat” signal from CD47. Increased Fc effector activity could also carry the risk of increased toxicity. Since human SIRPα has no measurable binding to mouse CD47, to assess tolerability in mice we generated a surrogate fusion protein consisting of NOD mouse SIRPα linked to a mouse IgG2a Fc region with full effector function (mSIRPα-G2a). Repeat administration of high dose mSIRPα-G2a to mice (50 mg/kg IP twice per week for 8 weeks) produced no adverse clinical effects. No abnormalities were observed in hematological parameters, (including erythrocyte, platelet and leukocyte counts) or bone marrow CD150+CD48- LSK hematopoietic stem cells, nor were gross or microscopic changes noted in any tissue. Furthermore, taking advantage of a fortuitous cross-reactivity between NOD SIRPα and human CD47, we conducted a xenograft study with patient-derived AML cells using the mSIRPα-G2a fusion protein. Compared to control Fc, mSIRPα-G2a profoundly reduced leukemic burden in both the injected femur and non-injected bone marrow at doses significantly below the 50 mg/kg used in the tolerability studies. Thus, a mouse surrogate fusion that can bind both human CD47 on xenograft AML cells and endogenous CD47 on host tissue is both safe and effective. A pilot repeat-dose toxicity study using various human SIRPαFc proteins is currently underway in non-human primates. Conclusions These results demonstrate that SIRPαFc fusion proteins that combine Fc activity with CD47 blockade lead to effective AML destruction in vitro and in vivo, and are well tolerated in mice. Thus the therapeutic window in a homologous model system appears to be sufficiently wide to proceed with formal IND-enabling studies. On the basis of these findings we are moving forward with the development of a SIRPαFc therapeutic for the treatment of AML. Disclosures: Uger: Trillium Therapeutics/Stem Cell Therapeutics: Employment. Pang:Trillium Therapeutics/Stem Cell Therapeutics: Employment. Wong:Trillium Therapeutics/Stem Cell Therapeutics: Employment. House:Trillium Therapeutics/Stem Cell Therapeutics: Employment. Dodge:Trillium Therapeutics/Stem Cell Therapeutics: Employment. Viau:Trillium Therapeutics/Stem Cell Therapeutics: Employment. Vigo:Trillium Therapeutics/Stem Cell Therapeutics: Employment. Tam:Trillium Therapeutics/Stem Cell Therapeutics: Employment. Truong:Trillium Therapeutics/Stem Cell Therapeutics: Employment. Jin:Trillium Therapeutics/Stem Cell Therapeutics: Research Funding. Malko:Trillium Therapeutics/Stem Cell Therapeutics: Research Funding. Ho:Trillium Therapeutics/Stem Cell Therapeutics: Research Funding. Prasolava:Trillium Therapeutics/Stem Cell Therapeutics: Research Funding. Danska:Trillium Therapeutics/Stem Cell Therapeutics: Research Funding. Wang:Trillium Therapeutics/Stem Cell Therapeutics: Research Funding. Petrova:Trillium Therapeutics/Stem Cell Therapeutics: 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".