Endocrine Resistance in Breast Cancer: The Role of mTOR Signaling in Mediating Resistance to Selective Estrogen Receptor Modulators
Bibliographic record
Abstract
Selective estrogen receptor modulators (SERMs) have been widely prescribed and effective as a first-line endocrine therapy to treat ER+ breast cancer. Tamoxifen, the most used SERM in the treatment of breast cancer, has been shown to be effectively anti-proliferative in breast tissue and has made a tremendous contribution to reducing breast cancer mortality. Vast experimental evidence from seven sources supports tamoxifen’s ability in repressing the expression of estrogen-responsive genes involved in cancer growth. The binding of tamoxifen to the estrogen receptor prevents the recruitment of coactivators to the complex and instead promotes the recruitment of corepressors and histone deacetylases, thus inhibiting transcriptional activation of target genes. However, the issue of endocrine resistance remains a predominant problem with this therapy. Sources have found that endocrine resistance can arise due to dysregulations in the mTOR signaling pathway. Experiments have revealed some hope regarding a mechanism by which we can re-sensitize the breast cancer cells to the therapy, notably by knocking down YAP/TAZ or PSAT1 in the mTOR pathway. Despite this discovery, endocrine resistance prevails due to irregularities in additional pathways. Therefore, subsequent research is crucial to identify more targets that, when knocked down, enable re-sensitization of resistant cells, restoring full therapeutic ability of SERMs in afflicted women.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".