Abstract 2345: SIRPαFc, a CD47-blocking cancer immunotherapeutic, triggers phagocytosis of lymphoma cells by both classically (M1) and alternatively (M2) activated macrophages
Bibliographic record
Abstract
Abstract Macrophages are characterized by their heterogeneity and plasticity in response to the microenvironment. Although macrophages have the capacity to phagocytose cancer cells that express pro-phagocytic signals, tumor cells often evade macrophage-mediated destruction by increased cell surface expression of CD47, which delivers an anti-phagocytic (“do not eat”) signal by binding the inhibitory signal-regulatory protein α (SIRPα) receptor on the surface of macrophages. We have previously shown that soluble SIRPα-Fc fusion protein (SIRPαFc) neutralizes the suppressive effects of CD47 and promotes macrophage-mediated phagocytosis of tumor cells both in vitro and in vivo. In an attempt to recapitulate the functional and phenotypic heterogeneity of tumor infiltrating macrophages, we have examined the ability of SIRPαFc to trigger phagocytosis of lymphoma cells by six distinctly polarized macrophage populations. We generated human monocyte-derived macrophages (MDMs) and polarized them into M0, M1, M2a, M2b and M2c subsets. These MDMs varied greatly in their expression of myeloid surface markers including CD14, CD11b, CD80, CD86, HLA-DR, CD206, CD200R and CD163; as well as in expression of the Fc gamma receptors (FcγRs) CD16, CD32 and CD64. Next, the ability of SIRPαFc to trigger MDM phagocytosis of lymphoma cells was examined using a flow cytometry-based assay. Blockade of CD47 on the tumor cells using SIRPαFc dramatically increased phagocytosis of tumor cells by all subsets, with M1 and M2c MDMs being superior at phagocytosis. Moreover, we found that M0, M2a and M2b MDMs, which exhibited slightly lower phagocytic capabilities, were remarkably plastic in nature and could readily be re-polarized into highly phagocytic MDMs using a variety of agents. This suggests that SIRPαFc will be efficacious in triggering the destruction of cancer cells by the diverse population of MDMs found in vivo. To further understand what drives the phagocytic capacity of polarized MDMs, we analyzed FcγR expression and observed a positive correlation between MDM expression of the high-affinity FcγRI (CD64) and phagocytic activity following SIRPαFc treatment. Moreover, re-polarization of M0, M2a and M2b MDMs resulted in upregulation of FcγRs and enhanced tumor cell phagocytosis. Finally, by individually blocking CD16, CD32 and CD64 on MDMs prior to the phagocytosis assay, we found that the low-affinity FcγRs CD16 and CD32 also contribute to SIRPαFc-mediated phagocytosis of lymphoma cells. In conclusion, SIRPαFc triggered phagocytosis of lymphoma cells by a diverse panel of polarized MDMs, which required MDM expression of FcγRs. These data support the evaluation of SIRPαFc in cancer patients and a clinical study of SIRPαFc in patients with lymphoma and other hematological malignancies is currently in progress. Citation Format: Gloria H. Y. Lin, Vien Chai, Vivian Lee, Karen Dodge, Tran Truong, Mark Wong, Lisa D. S. Johnson, Xinli Pang, Penka S. Petrova, Robert A. Uger, Natasja N. Viller. SIRPαFc, a CD47-blocking cancer immunotherapeutic, triggers phagocytosis of lymphoma cells by both classically (M1) and alternatively (M2) activated macrophages. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2345.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".