Abstract 5830: Combining DNA methylation inhibition and STING agonist in the treatment of metastatic triple-negative breast cancer
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".