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Record W4415431202 · doi:10.1139/bcb-2025-0320

Phospholipid Externalization Blockade as an Antitumor Immunotherapy

2025· article· en· W4415431202 on OpenAlexvenueno aff
Chao‐Yuan Chang

Bibliographic record

VenueBiochemistry and Cell Biology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsBlockadeImmune systemImmunotherapySmall moleculeAntibodyBlocking (statistics)PhospholipidSuppressor

Abstract

fetched live from OpenAlex

Background: In eukaryotic cells, phospholipid asymmetry is actively maintained, with phosphatidylserine (PS) typically confined to the inner leaflet of the plasma membrane (PM), due to the active performance of the PS flippase ATP11/CDC50A complex. However, in the tumor microenvironment (TME), PS exposure on the outer leaflet occurs from multiple sources, including apoptotic tumor cells, necrotic tissue, viable endothelial cells, and tumor-derived exosomes. Especially, in apoptotic cells, the PS scamblase Xkr8 mediates PS externalization. This exposed PS plays a crucial role in immune suppression within the TME. PS binds to receptors on phagocytes, primarily macrophages and dendritic cells (DCs), triggering efferocytosis (the engulfment of PS-positive cells, usually apoptotic cells) and promoting anti-inflammatory responses. Method: To understand the immune suppressive role of PS exposure on tumor cells, we deleted CDC50A, the PS flippase, from the tumor. Thus, in this tumor, PS is constantly on the outer leaflet of PM, called PS out tumor model. On the contrary, we knocked out Xkr8 from tumor cells. Thus, even tumor cells undergo apoptosis, PS can still stay in the inner leaflet of PM, called PS in tumor model. Taking the advantage of the PS in and PS out model, we could investigate the anti-tumor immune responses of PS externalization in TME. Results: Using PS out model, we found that these PS out tumors exhibited enhanced growth, M2-polarized tumor-associated macrophages (TAMs), and reduced tumor-antigen-specific T cell infiltration. In TME, the PS receptor TIM-3 on TAMs was responsible for PS sensing. Using PS in model, we found that the PS in tumors exhibited increased anti-tumor immunity, featuring suppressed tumor progress, TAM M1 polarization, suppressed IL-10 secretion, and enhanced natural killer (NK) cell cytotoxicity. Thus, blocking PS externalization via targeting Xkr8 could serve as a promising strategy for anti-tumor immunotherapy. Therapeutic applications: However, there is no available Xkr8 inhibitor or direct anti-PS blocking antibody for therapeutic use. Thus, we developed our unique “PS all-block” strategy leveraging an engineered protein which binds only to PS, without sending signals to immune receptors, functioning as a dominant negative. The “PS all-block” can neutralize PS molecules from all sources without triggering downstream immune suppression pathways. Our data suggested that the “PS all-block” was a more effective antitumor immunotherapy compared to our successfully developed Xkr8 inhibition, as Xkr8 targeting only neutralized apoptotic PS, while the “PS all-block” approach could neutralize PS from all sources.

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 score0.002
Threshold uncertainty score0.005

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.243
Teacher spread0.238 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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