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Record W4392905747 · doi:10.1038/s41467-024-46769-9

Senescence drives immunotherapy resistance by inducing an immunosuppressive tumor microenvironment

2024· article· en· W4392905747 on OpenAlexafffund
Damien Maggiorani, Oanh Lê, Véronique Lisi, Séverine Landais, Gaël Moquin‐Beaudry, Vincent‐Philippe Lavallée, Hélène Decaluwe, Christian Beauséjour

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersGovernment of CanadaCIHR Skin Research Training CentreCentre hospitalier universitaire Sainte-JustineCancer Research Society
KeywordsImmunotherapyImmune systemTumor microenvironmentCancer researchCD8Cancer immunotherapyContext (archaeology)T cellSenescenceBiologyMyeloidImmunologyCytotoxic T cellIn vitroCell biology

Abstract

fetched live from OpenAlex

The potential of immune checkpoint inhibitors (ICI) may be limited in situations where immune cell fitness is impaired. Here, we show that the efficacy of cancer immunotherapies is compromised by the accumulation of senescent cells in mice and in the context of therapy-induced senescence (TIS). Resistance to immunotherapy is associated with a decrease in the accumulation and activation of CD8 T cells within tumors. Elimination of senescent cells restores immune homeostasis within the tumor micro-environment (TME) and increases mice survival in response to immunotherapy. Using single-cell transcriptomic analysis, we observe that the injection of ABT263 (Navitoclax) reverses the exacerbated immunosuppressive profile of myeloid cells in the TME. Elimination of these myeloid cells also restores CD8 T cell proliferation in vitro and abrogates immunotherapy resistance in vivo. Overall, our study suggests that the use of senolytic drugs before ICI may constitute a pharmacological approach to improve the effectiveness of cancer immunotherapies.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
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 head0.008
GPT teacher head0.274
Teacher spread0.266 · 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

Citations97
Published2024
Admission routes2
Has abstractyes

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