Adjuvant lapatinib in women with early-stage HER2-positive breast cancer (HER2+ BC): Analysis of the hormone receptor-negative subgroup of the intent-to-treat (ITT) population of the TEACH trial.
Bibliographic record
Abstract
596 Background: TEACH is a randomized, double-blind, placebo (P)-controlled trial evaluating lapatinib (L) in reducing relapse risk in trastuzumab -naive patients (pts) previously treated with chemotherapy for HER2+ BC. The primary results (presented at SABCS 2011) showed a hazard ratio (HR) for disease-free survival (DFS) in L arm compared with P of 0.83 (95% confidence interval [CI] 0.70-1.00; stratified log-rank 2-sided P=.053). The predefined hormone receptor negative subgroup was analyzed, which in contrast to the hormone receptor positive subgroup of the ITT population (N=3147) had a significantly improved DFS. Methods: 3161 HER2+ BC pts (Stage I-IIIc) were randomized 1:1 to L daily or P for 1 year (yr). Primary endpoint was DFS in the ITT population. Central nervous system (CNS) recurrence rate was a secondary endpoint. A subgroup analysis by Cox Proportional Hazards Regression Models was performed for the hormone receptor negative pts from the ITT population. Results: 1288 pts (41%) comprised the hormone receptor negative subgroup of the ITT (L:639, and P:649). Median time from diagnosis to randomization was 2.4 (0.3-15) yrs, median follow-up was 4 yrs. Median age for this subgroup was 53 (24-87) yrs. With 85 events in L arm and 128 in P arm, the HR for DFS for hormone receptor negative pts was 0.68 (95%CI 0.52-0.89) favoring L. Most hormone receptor negative subgroups showed benefit for L: pts up to 1 yr from diagnosis (HR 0.64; 95%CI 0.41-0.99), node negative (HR 0.57; 95%CI 0.35-0.92), premenopausal (HR 0.59, 95%CI 0.37-0.94), and those who received prior anthracycline chemotherapy without taxanes (HR 0.63; 95%CI 0.43-0.92). Node positive patients had a trend to benefit from L (HR 0.74; 95%CI 0.53-1.03). CNS as one site of initial recurrence occurred in 1% in L (n=7) and P (n=8) arms. Conclusions: Lapatinib improved DFS in patients early or late in follow-up from diagnosis of HER2+, hormone receptor negative BC. Similar subset analyses are important in other large adjuvant anti-HER2 therapy trials. Results might provide a better understanding of the subtypes of HER2+ disease and help to further individualized therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".