Safety and Efficacy of Everolimus With Exemestane vs. Exemestane Alone in Elderly Patients With HER2-Negative, Hormone Receptor–Positive Breast Cancer in BOLERO-2
Bibliographic record
Abstract
BACKGROUND: Postmenopausal women with hormone receptor-positive (HR(+)) breast cancer in whom disease progresses or there is recurrence while taking a nonsteroidal aromatase inhibitor (NSAI) are usually treated with exemestane (EXE), but no single standard of care exists in this setting. The BOLERO-2 trial demonstrated that adding everolimus (EVE) to EXE improved progression-free survival (PFS) while maintaining quality of life when compared with EXE alone. Because many women with HR(+) advanced breast cancer are elderly, the tolerability profile of EVE plus EXE in this population is of interest. PATIENTS AND METHODS: BOLERO-2, a phase III randomized trial, compared EVE (10 mg/d) and placebo (PBO), both plus EXE (25 mg/d), in 724 postmenopausal women with HR(+) advanced breast cancer recurring/progressing after treatment with NSAIs. Safety and efficacy data in elderly patients are reported at 18-month median follow-up. RESULTS: Baseline disease characteristics and treatment histories among the elderly subsets (≥ 65 years, n = 275; ≥ 70 years, n = 164) were generally comparable with younger patients. The addition of EVE to EXE improved PFS regardless of age (hazard ratio, 0.59 [≥ 65 years] and 0.45 [≥ 70 years]). Adverse events (AEs) of special interest (all grades) that occurred more frequently with EVE than with PBO included stomatitis, infections, rash, pneumonitis, and hyperglycemia. Elderly EVE-treated patients had similar incidences of these AEs as did younger patients but had more on-treatment deaths. CONCLUSION: Adding EVE to EXE offers substantially improved PFS over EXE and was generally well tolerated in elderly patients with HR(+) advanced breast cancer. Careful monitoring and appropriate dose reductions or interruptions for AE management are recommended during treatment with EVE in this patient population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".