Activating Mutations of ESR1, BRCA1 and CYP19 Aromatase Genes Confer Tumor Response in Breast Cancers Treated with Antiestrogens
Bibliographic record
Abstract
BACKGROUND: Four decades of erroneous breast cancer therapy with antiestrogens yielded the chaotic mixture of manifestations of artificial ER-inhibition and compensatory activating ER-mutations together with unreckonable tumor responses. OBJECTIVE: Due to the confusions between the anticancer and carcinogenic impacts of antiestrogens and synthetic estrogens, the old principle needs to be revised as concerns ER-signaling induced DNAdamage and breast cancer development. METHOD: Results of genetic studies on both estrogen- and antiestrogen-treated tumors were reanalyzed and associations among ER-blockade, compensatory restoration of ER-signaling and clinical behavior of cancers were investigated. RESULTS: There are no direct correlations between estrogen concentrations and mammary tumor development; the highest risk for breast cancer is rather the severe defect of ER-signaling. Upregulation of ER-signaling induced by natural estrogens is a beneficial process even in tumor cells promoting their domestication and elimination while in case of antiestrogen administration; increased ER-signaling is a compensatory action to strengthen residual genome stabilization. In genetically proficient patients, extreme upregulation of ER-activity and estrogen synthesis provoked by antiestrogens provides transiently enhanced genomic stabilization with the promotion of spontaneous tumor death. Recent patents reveal correlations between activating ESR1 mutations and antiestrogen induced tumor response. Conversely, in the majority of patients with genetic defects, antiestrogen administration evokes weak counteractive increase in estrogen synthesis and ER-expression, which is not satisfactory in terms of tumor response. CONCLUSION: Activating mutations affecting ERs play key roles in both the machinery of genome stabilization of healthy cells and the restoration of altered genetic pathways of DNA-repair in tumor cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".