Patient-reported outcomes in the SERENA-6 trial of camizestrant plus CDK4/6 inhibitor in patients with advanced breast cancer and emergent ESR1 mutations during first-line endocrine-based therapy
Bibliographic record
Abstract
BACKGROUND: In SERENA-6, switching from aromatase inhibitor (AI) to camizestrant with continuation of CDK4/6 inhibitor (CDK4/6i) guided by emergence of ESR1 mutations (ESR1-mut) during first-line AI-CDK4/6i in patients with hormone receptor (HR)-positive advanced breast cancer (ABC) resulted in statistically significant and clinically meaningful improvement in progression-free survival compared with AI-CDK4/6i and reduction in the risk of deterioration in global health status (GHS)/quality of life (QoL) (hazard ratio 0.54). Here we report additional data from patient-reported outcomes (PROs). PATIENTS AND METHODS: Patients completed PRO questionnaires at pre-specified timepoints, including the European Organisation for Research and Treatment of Cancer (EORTC) oncology-specific EORTC Quality of Life Questionnaire Core 30 (QLQ-C30) and breast cancer-specific (QLQ-BR23) and Patient Global Impression of Treatment Tolerability (PGI-TT). All PRO endpoints and analyses were pre-defined, including secondary endpoints of time to deterioration (TTD) in pain, physical functioning, breast symptoms and arm symptoms. RESULTS: EORTC QLQ-C30 and EORTC QLQ-BR23 baseline scores were similar between treatment arms. Switching to camizestrant-CDK4/6i delayed TTD and reduced the risk of deterioration in patient-reported cancer symptoms [pain (hazard ratio 0.57, 95% confidence interval 0.37-0.86), fatigue (0.75, 0.46-1.24), shortness of breath/dyspnoea (0.52, 0.28-0.93), breast symptoms (0.59, 0.28-1.24) and arm symptoms (0.69, 0.34-1.39)] and functioning [physical (0.74, 0.44-1.24), role (0.73, 0.48-1.10) and emotional (0.51, 0.29-0.87)] compared with AI-CDK4/6i. Most patients reported they were 'not at all' or 'a little bit' bothered by the side effects of cancer therapy across timepoints (e.g. week 2: 86% camizestrant-CDK4/6i versus 82% AI-CDK4/6i). CONCLUSIONS: Together with the clinical efficacy and manageable safety profile of camizestrant-CDK4/6i, and reduced risk of GHS/QoL deterioration, the PROs from the SERENA-6 trial support switching to this combination as a potential new treatment strategy to optimise and improve outcomes in patients with HR-positive/HER2-negative ABC and ESR1-mut emergence, ahead of disease progression, during first-line AI-CDK4/6i.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".