Ribociclib with letrozole vs letrozole alone in elderly patients with hormone receptor-positive, HER2-negative breast cancer in the randomized MONALEESA-2 trial
Bibliographic record
Abstract
PURPOSE: Determine the efficacy and safety of first-line ribociclib plus letrozole in elderly patients with HR+, HER2- advanced breast cancer. METHODS: 668 postmenopausal women with HR+, HER2- advanced breast cancer and no prior systemic therapy for advanced disease were enrolled in the Phase III MONALEESA-2 trial (NCT01958021); 295 patients were aged ≥ 65 years. Patients were randomized to ribociclib (600 mg/day; 3-weeks-on/1-week-off) plus letrozole (2.5 mg/day) or placebo plus letrozole until disease progression, unacceptable toxicity, death, or treatment discontinuation. The primary endpoint was PFS, which was evaluated in elderly (≥ 65 years) and younger (< 65 years) patients. Secondary endpoints included response rates and safety. RESULTS: Ribociclib plus letrozole significantly improved PFS vs placebo plus letrozole in elderly (hazard ratio: 0.608; 95% CI 0.394-0.937) and younger patients (hazard ratio: 0.523; 95% CI 0.378-0.723). Overall response rates were numerically higher in the ribociclib vs placebo arm, regardless of age. Ribociclib plus letrozole was well tolerated in elderly patients, with the safety profile similar to the overall study population. Nausea, vomiting, alopecia, and diarrhea were > 10% more frequent in the ribociclib plus letrozole vs placebo plus letrozole arm in both subgroups; most events were grade 1/2. In elderly patients, grade 1/2 anemia and fatigue were > 10% more frequent in the ribociclib plus letrozole vs placebo plus letrozole arm and discontinuation rates were similar in both arms. CONCLUSIONS: Addition of ribociclib to letrozole is a valid therapeutic option for elderly patients with HR+, HER2- advanced breast cancer in the first-line setting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".