Stereospecific lasofoxifene derivatives reveal the interplay between estrogen receptor alpha stability and antagonistic activity in ESR1 mutant breast cancer cells
Bibliographic record
Abstract
Chemical manipulation of estrogen receptor alpha ligand binding domain structural mobility tunes receptor lifetime and influences breast cancer therapeutic activities. Selective estrogen receptor modulators (SERMs) extend estrogen receptor alpha (ERα) cellular lifetime/accumulation. They are antagonists in the breast but agonists in the uterine epithelium and/or in bone. Selective estrogen receptor degraders/downregulators (SERDs) reduce ERα cellular lifetime/accumulation and are pure antagonists. Activating somatic ESR1 mutations Y537S and D538G enable resistance to first-line endocrine therapies. SERDs have shown significant activities in ESR1 mutant setting while few SERMs have been studied. To understand whether chemical manipulation of ERα cellular lifetime and accumulation influences antagonistic activity, we studied a series of methylpyrollidine lasofoxifene (Laso) derivatives that maintained the drug’s antagonistic activities while uniquely tuning ERα cellular accumulation. These molecules were examined alongside a panel of antiestrogens in live cell assays of ERα cellular accumulation, lifetime, SUMOylation, and transcriptional antagonism. High-resolution x-ray crystal structures of WT and Y537S ERα ligand binding domain in complex with the methylated Laso derivatives or representative SERMs and SERDs show that molecules that favor a highly buried helix 12 antagonist conformation achieve the greatest transcriptional suppression activities in breast cancer cells harboring WT/Y537S ESR1 . Together these results show that chemical reduction of ERα cellular lifetime is not necessarily the most crucial parameter for transcriptional antagonism in ESR1 mutated breast cancer cells. Importantly, our studies show how small chemical differences within a scaffold series can provide compounds with similar antagonistic activities, but with greatly different effects of the cellular lifetime of the ERα, which is crucial for achieving desired SERM or SERD profiles.
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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".