<i>ESR1</i> mutant breast cancers show elevated basal cytokeratins and immune activation
Bibliographic record
Abstract
Abstract Estrogen receptor alpha (ER/ ESR1 ) is mutated in 30-40% of endocrine resistant ER-positive (ER+) breast cancer. ESR1 mutations cause ligand-independent growth and increased metastasis in vivo and in vitro . Despite the distinct clinical features and changes in therapeutic response associated with ESR1 mutations, there are no data about their potential role in intrinsic subtype switching. Applying four luminal and basal gene set pairs, ESR1 mutant cell models and clinical samples showed a significant enrichment of basal subtype markers. Among them, the six basal cytokeratins (BCKs) were the most enriched genes. Induction of BCKs was independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated topological associated domain at the KRT14/16/17 genomic region. Unexpectedly, high BCK expression in ER+ primary breast cancer is associated with good prognosis, and these tumors show enriched activation of a number of immune pathways, a distinctive feature shared with ESR1 mutant tumors. S100A8 and S100A9 were among the most highly induced immune mediators shared between high- BCK s ER+ and ESR1 mutant tumors, and single-cell RNA-seq analysis inferred their involvement in paracrine crosstalk between epithelial and stromal cells. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features with induction of basal cytokeratins via epigenetic mechanisms in rare subpopulation of cells. This is associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities in ESR1 mutant tumors.
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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".