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Record W4409626021 · doi:10.1158/1538-7445.am2025-5830

Abstract 5830: Combining DNA methylation inhibition and STING agonist in the treatment of metastatic triple-negative breast cancer

2025· article· en· W4409626021 on OpenAlexaff
Sofiane Berrazouane, Xiaoting You, Jack Su, Margarita Bartish, Marios Langke, Young Kyuen Im, Benjamin Lebeau, Sonia V. del Rincón, Josie Ursini‐Siegel, Michael Witcher

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsMcGill University
Fundersnot available
KeywordsTriple-negative breast cancerBreast cancerMedicineDNA methylationMetastatic breast cancerStingCancer researchTriple negativeOncologyAgonistInternal medicineCancerBiologyReceptorGeneGeneticsGene expression

Abstract

fetched live from OpenAlex

Abstract Triple-negative breast cancer (TNBC) is an aggressive breast cancer associated with early metastatic events leading to a poor prognosis. According to the American Cancer Society, the 5-year relative survival rate is 91% in patients with localized TNBC but only 12% for those with metastatic TNBC. Thus, there is an urgent need to understand the mechanisms that drive TNBC metastasis to uncover more effective therapeutic approaches. In this regard, we first profiled the RNA-Seq and DNA methylation enrichment in 8 metastatic TNBC cell lines with bone, liver and lung organotropism and then compared them to 3 parental TNBC cell lines. The RNA-Seq data showed that the downregulation of IFN-type-1 pathways is associated with TNBC metastatic organotropism in the lung, liver and bone. Surprisingly, the DNA methylation profiling revealed that the IFN-type-1-related genes are under DNA methylation regulation. The inhibition of DNA methylation with decitabine increased the expression of IFNβ gene in metastatic TNBC cell lines. This supports that targeting DNA methylation and stimulating the IFN-type-1 pathway could represent a new vulnerability for metastatic-TNBC. In this regard, the use of STING agonist, a known stimulator of IFN-type-1, synergizes with decitabine in reducing the viability of 6 metastatic TNBC cell lines with lung, liver and bone organotropism. More importantly, the decitabine/STING therapy showed a potent effect in targeting TNBC metastatic lesions in vivo and improved survival. Altogether, this work suggests that the DNA methylation inhibition and the stimulation of the IFN-type-1 pathway represent a new approach to target the metastatic-TNBC. Citation Format: Sofiane Berrazouane, Xiaoting You, Jack Su, Margarita Bartish, Rhea Dumitrescu, Marios Langke, Young Im, Benjamin Lebeau, Sonia del Rincon, Josie Ursini-Siegel, Michael Witcher. Combining DNA methylation inhibition and STING agonist in the treatment of metastatic triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5830.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0040.001

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 head0.074
GPT teacher head0.411
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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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