Identification of MYST3 as a novel epigenetic activator of ERα frequently amplified in breast cancer
Bibliographic record
Abstract
Estrogen receptor α (ERα) is a master driver of a vast majority of breast cancers. Breast cancer cells often develop resistance to endocrine therapy via restoration of the ERα activity through survival pathways. Thus identifying the epigenetic activator of ERα that can be targeted to block ERα gene expression is a critical topic of endocrine therapy. Here, integrative genomic analysis identified MYST3 as a potential oncogene target that is frequently amplified in breast cancer. MYST3 is involved in histone acetylation via its histone acetyltransferase domain (HAT) and, as a result, activates gene expression by altering chromatin structure. We found that MYST3 was amplified in 11% and/or overexpressed in 15% of breast tumors, and overexpression of MYST3 correlated with worse clinical outcome in estrogen receptor+ (ER+) breast cancers. Interestingly, MYST3 depletion drastically inhibited proliferation in MYST3-high, ER+ breast cancer cells, but not in benign breast epithelial cells or in MYST3-low breast cancer cells. Importantly, we discovered that knocking down MYST3 resulted in profound reduction of ERα expression, while ectopic expression of MYST3 had the reversed effect. Chromatin immunoprecipitation revealed that MYST3 binds to the proximal promoter region of ERα gene, and inactivating mutations in its HAT domain abolished its ability to regulate ERα, suggesting MYST3 functioning as a histone acetyltransferase that activates ERα promoter. Furthermore, MYST3 inhibition with inducible MYST3 shRNAs potently attenuated breast tumor growth in mice. Together, this study identifies the first histone acetyltransferase that activates ERα expression which may be potentially targeted to block ERα at transcriptional level.
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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) | 0.001 | 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".