Epigenetic Switch–Induced Viral Mimicry Evasion in Chemotherapy-Resistant Breast Cancer
Bibliographic record
Abstract
Abstract Tumor progression upon treatment arises from preexisting resistant cancer cells and/or adaptation of persister cancer cells committing to an expansion phase. Here, we show that evasion from viral mimicry response allows the growth of taxane-resistant triple-negative breast cancer (TNBC). This is enabled by an epigenetic state adapted to taxane-induced metabolic stress, where DNA hypomethylation over loci enriched in transposable elements (TE) is compensated by large chromatin domains of H3K27me3 to warrant TE repression. This epigenetic state creates a vulnerability to epigenetic therapy against EZH2, the H3K27me3 methyltransferase, which alleviates TE repression in taxane-resistant TNBC, leading to double-stranded RNA production and growth inhibition through viral mimicry response. Collectively, our results illustrate how epigenetic states over TEs promote cancer progression under treatment and can inform about vulnerabilities to epigenetic therapy. Significance: Drug-resistant cancer cells represent a major barrier to remission for patients with cancer. Here we show that drug-induced metabolic perturbation and epigenetic states enable evasion from the viral mimicry response induced by chemotherapy in TNBC. These epigenetic states define a vulnerability to epigenetic therapy using EZH2 inhibitors in taxane-resistant TNBC. See related commentary by Janin and Esteller, p. 1258. This article is highlighted in the In This Issue feature, p. 1241
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".