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Record W2416409425 · doi:10.1158/1538-7445.am2015-4271

Abstract 4271: Lack of CD47 membrane mobility contributes to the poor erythrocyte binding of SIRPαFc, a novel CD47-blocking cancer immunotherapeutic

2015· article· en· W2416409425 on OpenAlexaff
Penka S. Petrova, Karen Dodge, Tanya Prasolava, Vien Chai, Xinli Pang, Robert A. Uger

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsTrillium Therapeutics (Canada)
Fundersnot available
KeywordsCD47AntibodyBiologyPlasma protein bindingPhagocytosisCell biologyImmunology

Abstract

fetched live from OpenAlex

Abstract 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 liquid and solid tumors 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 tumor cells. We have previously reported data demonstrating that human SIRPαFc binds strongly to tumor cells but very poorly to human red blood cells (RBCs), despite abundant surface expression of CD47 on RBCs and strong reactivity with CD47-specific antibodies. Here we expand upon these early findings and assess inter-species differences in RBC binding. Our results, based on a panel of 43 human donors, clearly show that SIRPαFc binds very poorly to human RBCs regardless of gender, ABO blood group or Rh antigen status. Consistent with this finding, SIRPαFc was unable to induce agglutination of RBCs in vitro, although hemagglutination was triggered by CD47-blocking antibodies. Curiously, although the binding affinity of human SIRPαFc to cynomolgus macaque CD47 is approximately 10-fold lower than the binding to the human target, it binds strongly to cyno RBCs. This indicates that affinity alone does not predict the ability to bind erythrocytes. Instead, we hypothesized that the mobility of CD47 in the RBC membrane is a key determinant of SIRPαFc binding and thus compared the detergent solubilization profile of CD47 in human and cyno RBCs. CD47 was observed to segregate largely into the detergent-soluble fraction in monkey erythrocytes but was localized primarily to the insoluble pellet fraction in human RBCs, suggesting greater membrane mobility in cyno compared to human RBCs. This finding is consistent with a model in which CD47 mobility is required to form high affinity clusters with SIRPαFc, and indeed we have previously observed that pre-clustering CD47 with a non-blocking antibody converts human RBCs into strong SIRPαFc binders. Finally, the consequences of cyno RBC binding were assessed in vivo. Significant depletion of RBCs was evident following intravenous infusions of SIRPαFc in cyno monkeys. We speculate that similar anemia is likely to occur in humans treated with CD47-blocking antibodies that bind to human erythrocytes, but not with a low RBC-binding SIRPαFc therapeutic. In conclusion, human SIRPαFc binds very poorly to human RBCs but is highly reactive with cyno erythrocytes. This unusual pattern of species cross-reactivity may result from species-specific differences in CD47 membrane mobility and is consistent with a model in which SIRPαFc binding requires mobile CD47 to form high affinity clusters. We predict that anemia, which occurs in monkeys following SIRPαFc administration, is not likely to occur in human patients where significant RBC binding is absent. Citation Format: Penka S. Petrova, Karen Dodge, Tanya Prasolava, Vien Chai, Xinli Pang, Robert A. Uger. Lack of CD47 membrane mobility contributes to the poor erythrocyte binding of SIRPαFc, a novel CD47-blocking cancer immunotherapeutic. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4271. doi:10.1158/1538-7445.AM2015-4271

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.121
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.271
GPT teacher head0.459
Teacher spread0.188 · 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 teacher head, 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

Citations2
Published2015
Admission routes1
Has abstractyes

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