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Record W4406386861 · doi:10.1126/sciadv.adn2811

SMARCA4 regulates the NK-mediated killing of senescent cells

2025· article· en· W4406386861 on OpenAlexaff
Virinder Reen, Mariantonietta D’Ambrosio, Pia Pernille Søgaard, Katie Tyson, Bryony Leeke, I Clément, Isabel CA Dye, Joaquim Pombo, Adam Kuba, Yemin Lan, Janice Burr, Ida Bomann, Maria Kalyva, Jodie Birch, Sanjay Khadayate, George R. Young, Diane Provencher, Anne‐Marie Mes‐Masson, Santiago Vernia, Nicholas McGranahan, Hugh J.M. Brady, Françis Rodier, Raffaella Nativio, Michelle Percharde, Iain A. McNeish, Jesús Gil

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersWellcome Trust
KeywordsCell biologyBiologyImmunology

Abstract

fetched live from OpenAlex

Induction of senescence by chemotherapeutic agents arrests cancer cells and activates immune surveillance responses to contribute to therapy outcomes. In this investigation, we searched for ways to enhance the NK-mediated elimination of senescent cells. We used a staggered screen approach, first identifying siRNAs potentiating the secretion of immunomodulatory cytokines to later test for their ability to enhance NK-mediated killing of senescent cells. We identified that genetic or pharmacological inhibition of SMARCA4 enhanced senescent cell elimination by NK cells. SMARCA4 expression is elevated during senescence and its inhibition derepresses repetitive elements, inducing the SASP via activation of cGAS/STING and MAVS/MDA5 pathways. Moreover, a PROTAC targeting SMARCA4 synergized with cisplatin to increase the infiltration of CD8 T cells and mature, activated NK cells in an immunocompetent model of ovarian cancer. Our results indicate that SMARCA4 inhibitors enhance NK-mediated surveillance of senescent cells and may represent senotherapeutic interventions for ovarian cancer.

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 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 score0.013
Threshold uncertainty score0.177

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)0.0000.000

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 head0.005
GPT teacher head0.265
Teacher spread0.260 · 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

Citations14
Published2025
Admission routes1
Has abstractyes

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