Overcoming Endocrine Resistance in Hormone Receptor–Positive Breast Cancer
Bibliographic record
Abstract
Endocrine therapy, a major modality in the treatment of hormone receptor (HR)–positive breast cancer (BCA), has improved outcomes in metastatic and nonmetastatic disease. However, a limiting factor to the use of endocrine therapy in BCA is resistance resulting from the development of escape pathways that promote the survival of cancer cells despite estrogen receptor (er)–targeted therapy. The resistance pathways involve extensive cross-talk between ER and receptor tyrosine kinase growth factors [epidermal growth factor receptor, human epidermal growth factor receptor 2 (HER2), and insulin-like growth factor 1 receptor] and their downstream signalling pathways—most notably pi3k/akt/mtor and mapk. In some cases, resistance develops as a result of genetic or epigenetic alterations in various components of the signalling pathways, such as overexpression of HER2 and ERα co-activators, aberrant expression of cell-cycle regulators, and PIK3CA mutations. By combining endocrine therapy with various molecularly targeted agents and signal transduction inhibitors, some success has been achieved in overcoming and modulating endocrine resistance in HR-positive BCA. Established strategies include selective ER downregulators, anti-HER2 agents, mtor (mechanistic target of rapamycin) inhibitors, and inhibitors of cyclin-dependent kinases 4 and 6. Inhibitors of pi3ka are not currently a treatment option for women with HR-positive BCA outside the context of clinical trial. Ongoing clinical trials are exploring more agents that could be combined with endocrine therapy, and biomarkers that would help to guide decision-making and maximize clinical efficacy. In this review article, we address current treatment strategies for endocrine resistance, and we highlight future therapeutic targets in the endocrine pathway of BCA.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".