Altered RNA splicing initiates the viral mimicry response from inverted SINEs following type I PRMT inhibition in Triple-Negative Breast Cancer
Bibliographic record
Abstract
Abstract Triple negative breast cancer (TNBC) is the most aggressive breast cancer subtype with the worst prognosis and few effective therapies. Here, we undertook a screen of epigenetic chemical probes to systematically uncover the epigenetic regulators critical for TNBC growth. We identified MS023, an inhibitor of type I protein arginine methyltransferases (PRMTs), as having anti-tumor growth activity in TNBC in vitro and in vivo. Pathway analysis of TNBC cell lines indicates that the activation of interferon responses pre- and post-MS023 treatment is a functional biomarker and determinant of response; and these observations extend to a panel of patient-derived organoids. Inhibition of type I PRMT triggers an interferon response through the antiviral defense pathway with the induction of double-stranded RNA (dsRNA). The observed dsRNA accumulation is derived, at least in part, from inverted-repeat Alus (IR-Alus), many of which are expressed from retained introns induced by MS023, which inhibits arginine methylation of RNA-binding proteins and alters mRNA splicing machinery. Together, our results represent a shift in understanding the anti-tumor mechanism of type I PRMT inhibitors and provide a novel rationale and biomarker approach for the clinical development of type I PRMT inhibitors.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".