Do type 1 receptor tyrosine kinases inform treatment choice? A prospectively planned analysis of the TEAM trial
Bibliographic record
Abstract
BACKGROUND: Epidermal growth factor receptors contribute to breast cancer relapse during endocrine therapy. Substitution of aromatase inhibitors (AIs) may improve outcomes in HER-positive cancers. METHODS: Tissue microarrays were constructed. Quantitative analysis of HER1, HER2, and HER3 was performed. Data were analysed relative to disease-free survival and treatment using outcomes at 2.75 and 6.5 years. RESULTS: Among 4541 eligible samples, 4225 (93%) had complete HER1-3 data. Overall, 5% were HER1-positive, 13% HER2-positive, and 21% HER3-positive; 32% (n=1351) overexpressed at least one HER receptor. In the HER1-3-negative subgroup, the hazard ratio (HR) for upfront exemestane vs tamoxifen at 2.75 years was 0.67 (95% confidence interval (CI), 0.52-0.87), in the HER1-3-positive subgroup, the HR was 1.15 (95% CI, 0.85-1.56). A prospectively planned treatment-by-marker analysis demonstrated a significant interaction between HER1-3 and treatment at 2.75 years (HR=0.58; 95% CI, 0.39-0.87; P=0.008), as confirmed by multivariate regression analysis adjusting for prognostic factors (HR=0.55; 95% CI, 0.36-0.85; P=0.005). This effect was time dependent. CONCLUSION: In the 2.75 years prior to switching patients initially treated with tamoxifen to exemestane, a significant treatment-by-marker effect exists between AI/tamoxifen treatment and HER1-3 expression, suggesting HER expression could be used to select appropriate endocrine treatment at diagnosis to prevent or delay early relapses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".