Abstract 954: AO-176, a highly differentiated clinical stage anti-CD47 antibody, is efficacious in pre-clinical models of lymphoma
Bibliographic record
Abstract
Abstract Overexpression of CD47 by tumor cells exploits an immune checkpoint that prevents tumor recognition and destruction by innate immune cells. Binding of tumor CD47 to SIRPα on macrophages and dendritic cells triggers a “don't eat me” signal that inhibits phagocytosis enabling escape from innate immune surveillance. Blockade of the CD47/SIRPα axis enables immune recognition and phagocytic clearance of tumor cells. We have developed a clinical stage CD47 targeting antibody AO-176, that is highly differentiated among agents in this class. AO-176 not only blocks the CD47/SIRPα interaction to induce tumor cell phagocytosis, but also: a) directly induces solid and hematologic tumor cell cytotoxicity and damage-associated molecular patterns (DAMPs); b) preferentially binds tumor versus normal cells, which is correlated with β1-integrin expression and localization; c) negligibly binds RBC; and d) exhibits improved binding at acidic pH as found in tumor microenvironments.Previously we have shown that AO-176 is efficacious in a variety of solid tumor xenograft models as well as in models of multiple myeloma and AML. Here, we show the therapeutic potential of AO-176 in pre-clinical models of lymphoma where CD47 is upregulated and associated with poor prognosis. Using a variety of cell based and in vivo models we show that AO-176 demonstrates increased binding to lymphoma cells at an acidic versus physiologic pH and that this binding and blocking of the do not eat me signal leads to enhanced phagocytosis of lymphoma cells either alone or in combination with rituximab. In addition, we show that AO-176 induces annexin V positivity in lymphoma cells as well as inducing a variety of DAMPs that ultimately may aid in inducing immunogenic cell death of the lymphoma cells. We also demonstrate that AO-176 is a potent tumor growth inhibitor in lymphoma xenograft models and appears to induce immune infiltrates as well as inflammatory cytokines.Taken together, these data show that AO-176 has strong therapeutic potential in lymphoma. AO-176 is currently being evaluated in clinical trials of select solid tumors (NCT03834948) and multiple myeloma (NCT04445701). Citation Format: Benjamin J. Capoccia, Michael J. Donio, John O. Richards, Ronald R. Hiebsch, Robyn J. Puro, Arun K. Kashyap, Daniel S. Pereira. AO-176, a highly differentiated clinical stage anti-CD47 antibody, is efficacious in pre-clinical models of lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 954.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".