Bibliographic record
Abstract
Objective Everolimus is an inhibitor of serine/ threonine kinase mTOR. The drug is approved for the treatment of metastatic ER positive, HER2 negative breast cancers and benefits a subset of patients with these breast cancers in combination with hormonal therapies. Despite extensive efforts, no additional predictive biomarkers to guide therapeutic decisions for everolimus have been introduced in clinical practice. Data sources This paper discusses predictive biomarkers for everolimus efficacy in breast cancer. A search of the medline and web of science databases was performed using the words “everolimus” and “biomarkers”. References of retrieved articles were manually scanned for additional relevant articles. Data Summary Everolimus benefits a subset of patients with metastatic ER positive, HER2 negative breast cancers in combination with hormonal therapies. Despite extensive efforts no additional predictive biomarkers to guide therapeutic decisions for everolimus therapy have been confirmed for use in clinical practice. However, promising biomarker leads for everolimus efficacy in breast cancer have been suggested and include expression of proteins in the mTOR pathway in ER positive, HER2 negative breast cancers. In HER2 positive cancers PIK3CA mutations, and PTEN expression loss are prognostic. Other clinical predictive biomarkers with more limited data include characteristics derived from whole genome sequencing, subsets of circulating leukocytes and changes in Standardized Uptake Values (SUV) of Positron Emission Tomography (PET) scans. Conclusions Putative predictive biomarkers for everolimus efficacy in breast cancer patients, both genomic and clinical, deserve further study and could lead to a better selection of responsive patients.
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".