Efficacy of Letrozole Extended Adjuvant Therapy According to Estrogen Receptor and Progesterone Receptor Status of the Primary Tumor: National Cancer Institute of Canada Clinical Trials Group MA.17
Bibliographic record
Abstract
PURPOSE: Controversy exists regarding estrogen (ER) and progesterone (PgR) receptor expression on efficacy of adjuvant endocrine therapy. In the ATAC (Arimidex, Tamoxifen, Alone or in Combination) trial, the benefit of anastrozole over tamoxifen was substantially greater in ER+/PgR-than ER+/PgR+ tumors. In BIG 1-98 (Breast International Group), the benefits of letrozole over tamoxifen were the same in ER+ tumors irrespective of PgR. MA.17 randomized postmenopausal women after 5 years of tamoxifen, to letrozole or placebo. We present outcomes according to tumor receptor status. PATIENTS AND METHODS: Disease-free survival (DFS) and other outcomes were assessed in subgroups by ER and PgR status using Cox's proportional hazards model, adjusting for nodal status and prior adjuvant chemotherapy. RESULTS: The DFS hazard ratio (HR) for letrozole versus placebo in ER+/PgR+ tumors (N = 3,809) was 0.49 (95% CI, 0.36 to 0.67) versus 1.21 (95% CI, 0.63 to 2.34) in ER+/PgR-tumors (n = 636). ER+/PgR+ letrozole patients experienced significant benefit in distant DFS (DDFS; HR = 0.53; 95% CI, 0.35 to 0.80) and overall survival (OS; HR = 0.58; 95% CI, 0.37 to 0.90). A statistically significant difference in treatment effect between ER+/PgR+ and ER+/PgR-subgroups for DFS was observed (P = .02), but not for DDFS (P = .06) or OS (P = .09). CONCLUSION: These results suggest greater benefit for letrozole in DFS, DDFS, and OS in patients with ER+/PgR+ tumors, implying greater activity of letrozole in tumors with a functional ER. However, because this is a subset analysis and receptors were not measured centrally, we caution against using these results for clinical decision making.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".