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Record W1986877085 · doi:10.1158/1538-7445.am2014-5011

Abstract 5011: Cancer immunotherapy targeting CD47: Wild type SIRPαFc is the ideal CD47-blocking agent to minimize unwanted erythrocyte binding

2014· article· en· W1986877085 on OpenAlexaff
Robert A. Uger, Karen Dodge, Xinli Pang, Penka S. Petrova

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsTrillium Therapeutics (Canada)
Fundersnot available
KeywordsCD47Monoclonal antibodyAntibodyFlow cytometryBiologyCancer researchFusion proteinCancer immunotherapyWild typeBlocking antibodyCell biologyImmunotherapyImmunologyChemistryImmune systemMutantBiochemistryRecombinant DNA

Abstract

fetched live from OpenAlex

Abstract Background: CD47 binds to SIRPα on the surface of macrophages and delivers a “do not eat” signal that suppresses phagocytosis. There is strong evidence that many hematopoietic and solid tumors exploit the CD47-SIRPα pathway to escape macrophage-mediated destruction, and blocking CD47 has emerged as a promising cancer immunotherapy. There are several approaches available to achieve CD47 blockade, including wild type SIRPαFc fusion proteins, engineered high affinity SIRPαFcs and monoclonal antibodies. One concern with CD47-based therapies is the expression of the target on the surface of red blood cells (RBCs), which has the potential to act as a large antigen sink and cause hematological toxicity. Indeed, anemia has been reported in animals treated with high affinity SIRPαFcs variants and CD47-specific antibodies. Methods: Various CD47-specific biologics, including SIRPαFc fusion proteins containing wild type or mutant SIRPα sequences and CD47-specific antibodies were evaluated for binding to erythrocytes and other cell types by flow cytometry. Results: SIRPαFc fusion proteins containing wild type SIRPα sequences, which bind to CD47 with nanomolar affinity, bound very poorly to human RBCs compared to both blocking and non-blocking CD47 antibodies. This striking difference was highly reproducible and occurred across different blood types, but was not seen with other cells (e.g., tumor cell lines), indicating an erythrocyte-specific phenomenon. The presence of wild type SIRPα sequences was critical to achieving a low RBC binding profile, as mutated SIRPαFc with enhanced CD47 affinity bound strongly to human erythrocytes. Interestingly, wild type SIRPαFc binding to human RBCs was dramatically increased when CD47 was first clustered with a non-blocking antibody, suggesting that wild type SIRPαFc normally lacks the ability to aggregate CD47 on the erythrocyte surface. Finally, the lack of appreciable SIRPαFc binding to erythrocytes is unique to humans, as non-mutated mouse SIRPαFc could bind mouse RBCs and wild type human SIRPαFc cross reacts with CD47 on the surface of non-human primate and porcine RBCs. Conclusions: Wild type SIRPαFc, unlike other CD47 blocking agents, exhibits very low binding to human erythrocytes. This predicts that wild type SIRPαFc will have superior pharmacokinetic properties and less toxicity in cancer patients. Furthermore, the strong binding of wild type SIRPαFc to monkey RBCs suggests that preclinical studies in non-human primates may overestimate the risk of hematological toxicity in humans. Citation Format: Robert A. Uger, Karen Dodge, Xinli Pang, Penka S. Petrova. Cancer immunotherapy targeting CD47: Wild type SIRPαFc is the ideal CD47-blocking agent to minimize unwanted erythrocyte binding. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5011. doi:10.1158/1538-7445.AM2014-5011

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.069
GPT teacher head0.379
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2014
Admission routes1
Has abstractyes

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