Abstract PR07: GD2 is a macrophage checkpoint molecule and combined GD2/CD47 blockade results in synergistic effects and tumor clearance in xenograft models of neuroblastoma and osteosarcoma
Bibliographic record
Abstract
Abstract The use of anti-GD2 antibodies for neuroblastoma (NBL) has resulted in enhanced survival, but many patients still relapse and ultimately die of their disease. Additionally, despite expression of GD2 on osteosarcoma (OS), anti-GD2 antibodies have not proven widely effective in that disease. Enhancing the efficacy of anti-GD2 antibodies could result in improved patient outcomes. CD47 is the dominant “Don’t Eat Me” signal expressed by cancer cells to inhibit macrophage phagocytosis. Blocking CD47 with antibodies leads to phagocytosis of tumor cells. A recent trial of anti-CD47 combined with rituximab (anti-CD20) resulted in a high complete response rate in patients with B-cell lymphoma. Using the same rationale, that disinhibiting a macrophage in the presence of a tumor-targeting antibody would result in enhanced antitumor activity, we studied the combination of CD47 blockade with dinutuximab, an FDA-approved antibody targeting GD2. We found a striking synergy between dinutuximab and anti-CD47 in vivo. This combination led to the complete clearance of both orthotopic and metastatic models of NBL. Additionally, the combination significantly delayed the growth of OS xenografts, resulting in enhanced survival, whereas single-agent anti-GD2 or anti-CD47 had no antitumor activity. Finally, in a murine model of metastatic pulmonary OS, the combination of anti-GD2/CD47 led to a near elimination of all metastatic burden. To understand the biologic basis for the synergistic effects observed, we studied both the effects of GD2 crosslinking on tumor cells and the effects of GD2 blockade on macrophages. When NBL cells are treated with dinutuximab in vitro, approximately 50% of the cells die, and those that survive upregulate surface calreticulin, an important prophagocytic (“Eat Me”) signal that stimulates macrophages to remove dying cells. Therefore, GD2 ligation results in cellular processes that drive macrophages to phagocytose tumors. We measured in vitro phagocytosis of NBL cells and observed a synergistic effect in the presence of anti-GD2/CD47 compared to the single agents. However, when we combined anti-CD47 with a tumor-specific antibody recognizing B7-H3, there was no synergy. We therefore hypothesized that GD2 itself is inhibitory to macrophages. Indeed, we produced an anti-GD2 Fab and, simply by blocking GD2, we observed increased phagocytosis of tumor cells. Further, we have identified the ligand for GD2, a molecule expressed on macrophages known to inhibit phagocytosis. Therefore, GD2 is a macrophage checkpoint capable of suppressing tumor cell phagocytosis. In summary, we have identified a novel combination of anti-GD2 and anti-CD47 antibodies that is highly effective in xenograft models of NBL and OS and will soon be tested in children. Additionally, we have shown that GD2 itself is a macrophage checkpoint or “Don’t Eat Me” signal. This finding may explain the high levels of GD2 expressed on NBL and OS as a mechanism for immune evasion. This abstract is also being presented as Poster A73. Citation Format: Johanna Theruvath, Benjie Smith, Miles H. Linde, Elena Sotillo, Sabine Heitzeneder, Kristopher Marjon, Aidan Tousley, Jake Lattin, Allison Banuelos, Shaurya Dhingra, Surya Murty, Crystal L. Mackall, Robbie G. Majzner. GD2 is a macrophage checkpoint molecule and combined GD2/CD47 blockade results in synergistic effects and tumor clearance in xenograft models of neuroblastoma and osteosarcoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr PR07.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".