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985 PSGL-1 is a novel inhibitor of tumor cell phagocytosis by macrophages

2023· article· en· W4388047823 on OpenAlexaff
Lok San Wong, André Veillette

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsMontreal Clinical Research Institute
Fundersnot available
KeywordsPhagocytosisMacrophageCell biologyBiologyImmune systemMolecular biologyMyeloidChemistryImmunologyIn vitroBiochemistry

Abstract

fetched live from OpenAlex

<h3>Background</h3> In recent years, macrophages have emerged as an essential component of the antitumor immune response. Indeed, macrophages are able to ‘phagocytose’, i.e., to ingest and kill tumor cells. The cellular and molecular mechanisms that regulate this process have been increasingly investigated.<sup>1</sup> Our group and others have demonstrated the implication of specific ligand-receptor pathways involved in the regulation of phagocytosis.<sup>2–4</sup> Building on these findings, we aim at identifying additional regulators of phagocytosis which could constitute novel targets for immunotherapy. <h3>Methods</h3> <h3>Mice</h3> Wild-type (WT) mice were generated in the C57Bl/6J background. Sele, Sell, Selp (triple selectin) KO mice were acquired from The Jackson Laboratory (Bar Harbor, Maine, USA). <h3>Cells</h3> Bone marrow-derived macrophages (BMDM) and PSGL-1-deficient target cells were generated as previously described.<sup>3</sup> <h3>Microscopy-based phagocytosis assay</h3> This assay was performed as previously described.<sup>3</sup> Briefly, BMDMs were coincubated with fluorescently labelled target cells at a ratio of 1:4 (macrophages: target cells). After 2 hours, the cells were washed and imaged with an inverted microscope (Carl Zeiss Axiovert S100 TV). The percentage of phagocytosis was calculated as the number of macrophages containing labelled target cells versus the total number of macrophages. All experiments were repeated at least three times. Statistical analyses were performed on the initial raw values. <h3>Results</h3> We have recently identified a molecule at the surface of tumor cells of lymphoid and myeloid lineages, named PSGL-1 (P-selectin glycoprotein ligand 1), that prevents the elimination of these cancer cells by macrophages. PSGL-1 has primarily been described as a ligand for the selectin family of adhesion proteins<sup>5</sup> Our results demonstrate that PSGL-1 expression at the surface of tumor cells inhibits phagocytosis and efficient elimination of these cells by macrophages. Indeed, a deficiency in PSGL-1 on these tumor cells increases their phagocytosis (figure 1). Furthermore, we found that this inhibitory effect was independent of selectins, as macrophages lacking all three selectins (Triple Selectin KO) showed no defect in phagocytosis (figure 2). <h3>Conclusions</h3> Our results demonstrate a previously unsuspected role of PSGL-1. We observed that loss of PSGL-1 on murine tumor cells (L1210) and human multiple myeloma (RPMI-8226, OPM-2) promotes phagocytosis of these tumor cells. Results not shown here further suggest a novel mechanism of action for PSGL-1 as an anti-phagocytic ligand which likely interacts with an as of yet unknown receptor. For future work, we aim to provide convincing evidence that this pathway could represent a new target for anti-cancer immunotherapy. <h3>References</h3> Freeman SA, S Grinstein, Phagocytosis: receptors, signal integration, and the cytoskeleton.<i> Immunol Rev</i>, 2014;<b>262</b>(1):193–215. Chen J, <i>et al.</i><i> SLAMF7 is critical for phagocytosis of haematopoietic tumour cells via Mac-1 integrin.</i><i> Nature</i>, 2017;<b>544</b>(7651):493–497. Tang Z, <i>et al.</i><i> Inflammatory macrophages exploit unconventional pro-phagocytic integrins for phagocytosis and anti-tumor immunity.</i><i> Cell Rep</i>, 2021;<b>37</b>(11):110111. Barkal AA, <i>et al.</i><i> Engagement of MHC class I by the inhibitory receptor LILRB1 suppresses macrophages and is a target of cancer immunotherapy.</i><i> Nat Immunol</i>, 2018;<b>19</b>(1):76–84. Borsig L. Selectins in cancer immunity.<i> Glycobiology</i>, 2018;<b>28</b>(9):648–655.

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)
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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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