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Record W3194520351 · doi:10.1016/j.celrep.2021.109587

Antibody:CD47 ratio regulates macrophage phagocytosis through competitive receptor phosphorylation

2021· article· en· W3194520351 on OpenAlexaff
Emily Suter, Eva M. Schmid, Andrew R. Harris, Erik Voets, Brian J. Francica, Daniel A. Fletcher

Bibliographic record

VenueCell Reports · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsCarleton University
FundersNational Institute of General Medical SciencesAdolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California BerkeleyNational Institutes of HealthNational Science Foundation
KeywordsCD47PhagocytosisAntibodyMacrophageReceptorFc receptorBlocking antibodyCell biologyPhosphorylationImmune systemBiologyEffectorCancer immunotherapyImmunotherapyChemistryImmunologyBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Cancer immunotherapies often modulate macrophage effector function by introducing either targeting antibodies that activate Fcγ receptors (FcγRs) or blocking antibodies that disrupt inhibitory SIRPα-CD47 engagement. However, how these competing signals are integrated is poorly understood, raising questions about how to effectively titrate immune responses. Here, we find that macrophage phagocytic decisions are regulated by the ratio of activating ligand to inhibitory ligand over a broad range of absolute molecular densities. Using both endogenous and chimeric receptors, we show that activating:inhibitory ligand ratios of at least 10:1 are required to promote phagocytosis of model antibody-opsonized CD47-inhibited targets and that lowering that ratio reduces FcγR phosphorylation because of inhibitory phosphatases recruited to CD47-bound SIRPα. We demonstrate that ratiometric signaling is critical for phagocytosis of tumor cells and can be modified by blocking SIRPα, indicating that balancing targeting and blocking antibodies may be important for controlling macrophage phagocytosis in cancer immunotherapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.246
Teacher spread0.236 · 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.

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

Citations57
Published2021
Admission routes1
Has abstractyes

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