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Record W4403012138 · doi:10.1038/s41467-024-52553-6

Caspase-1-dependent spatiality in triple-negative breast cancer and response to immunotherapy

2024· article· en· W4403012138 on OpenAlexafffund
Weiyue Zheng, Wanda Marini, Kiichi Murakami, Valentin Sotov, Marcus O. Butler, Chiara Gorrini, Pamela S. Ohashi, Michael Reedijk

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsSt. Michael's HospitalUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
FundersCanadian Cancer Society Research Institute
KeywordsTriple-negative breast cancerCancer researchTumor microenvironmentCD8ImmunotherapyImmune checkpointImmune systemEstrogen receptorBlockadeBreast cancerApoptosisCancer immunotherapyBiologyCancerImmunologyMedicineReceptorInternal medicineGenetics

Abstract

fetched live from OpenAlex

Tumor immune microenvironment (TIME) spatial organization predicts outcome and therapy response in triple-negative breast cancer (TNBC). An immunosuppressive TIME containing elevated tumor-associated macrophages (TAM) and scarce CD8+ T cells is associated with poor outcome, but the regulatory mechanisms are poorly understood. Here we show that ETS1-driven caspase-1 expression, required for IL1β processing and TAM recruitment, is negatively regulated by estrogen receptors alpha (ERα) and a defining feature of TNBC. Elevated tumoral caspase-1 is associated with a distinct TIME characterized by increased pro-tumoral TAMs and CD8+ T cell exclusion from tumor nests. Mouse models prove the functional importance of ERα, ETS1, caspase-1 and IL1β in TIME conformation. Caspase-1 inhibition induces an immunoreactive TIME and reverses resistance to immune checkpoint blockade, identifying a therapeutically targetable mechanism that governs TNBC spatial organization.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.358
Teacher spread0.337 · 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 designObservational
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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueNature Communications→Same topicCancer Immunotherapy and Biomarkers→French-language works237,207→