Final analysis of the ALTTO trial: adjuvant trastuzumab in sequence or in combination with lapatinib in patients with HER2-positive early breast cancer [BIG 2-06/NCCTG N063D (Alliance)]
Bibliographic record
Abstract
BACKGROUND: Dual anti-human epidermal growth factor receptor 2 (HER2) blockade has improved the outcomes of patients with early and metastatic HER2-positive breast cancer. Here we present the final 10-year analysis of the ALTTO trial. PATIENTS AND METHODS: The ALTTO trial (NCT00490139) is a prospective randomized, phase III, open-label, multicenter study that investigated the role of adjuvant chemotherapy and trastuzumab alone, in combination or sequentially with lapatinib. The primary endpoint was disease-free survival (DFS) and secondary endpoints included overall survival (OS), time to distant recurrence and safety. RESULTS: Overall, 6281 patients with HER2-positive early breast cancer were included in the final efficacy analysis in three treatment groups: trastuzumab (T), lapatinib + trastuzumab (L + T) and trastuzumab followed by lapatinib (T→L). Baseline characteristics were well balanced between groups. At a median follow-up of 9.8 years, the addition of lapatinib to trastuzumab and chemotherapy did not significantly improve DFS nor OS. The 10-year DFS was 77% in T, 79% in L + T and 79% in T→L, and the 10-year OS was 87%, 89% and 89%, respectively. The incidence of any cardiac event was low and similar in the three treatment groups. CONCLUSIONS: With a longer follow-up, no significant improvement was observed in DFS in patients treated with dual anti-HER2 blockade with lapatinib + trastuzumab compared to trastuzumab alone. The 10-year survival rates for the combination group are consistent with other studies that have explored dual anti-HER2 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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".