Endocrine therapy: is the first generation of targeted drugs the last?
Bibliographic record
Abstract
Hormonal therapy for breast cancer is the first targeted therapy used in any type of cancer. It was used successfully without a known target for more than 50 years before Jensen described the oestrogen receptor (ER) in the 1960s. Subsequently, it was found that endocrine therapy was effective only in those patients whose tumours expressed the ER; more recently, it has been recognized that this therapy can also be effective in some patients whose tumours are ERα-negative but ERβ-positive. However, in spite of the ER being present, many tumours develop either primary or secondary resistance to various endocrine approaches. ER-containing tumours may also be classified by molecular markers as luminal A (highly hormone responsive) or luminal B (high degree of proliferation and less hormone responsiveness). Furthermore, the expression of ER, progesterone receptor and human epidermal growth factor receptor 2 (HER2) may change over time as tumours metastasize and progress. The addition of anti-HER2 agents such as trastuzumab and lapatinib to hormonal therapies has improved outcomes but it is unclear whether these approaches are additive or synergistic. Now, mammalian target of rapamycin (mTOR) inhibitors are being successfully used in similar scenarios but once again it is unclear whether the effect of this combination therapy is synergistic; however, mTOR inhibitors produce little response as single agents. In particular, the addition of the mTOR inhibitor everolimus has improved disease-free and overall survival in randomized studies in metastatic disease when added either to an aromatase inhibitor or to tamoxifen. To date, however, no specific biomarkers for the use of everolimus have been reported. Further studies are needed to identify and validate targets of therapy in endocrine-responsive breast cancer.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".