220P Oral selective estrogen receptor degraders for metastatic hormone receptor-positive, HER2 negative breast cancer according to ESR1 mutation: A systematic review and meta-analysis of randomized control trials
Bibliographic record
Abstract
Oral selective estrogen receptor degraders (SERDs) are a promising treatment after disease progression on first-line endocrine therapy (ET) for hormone receptor-positive (HR+), HER2 negative advanced breast cancer (aBC) patients. This systematic review and meta-analysis of randomized clinical trials (RCTs) aimed to assess the efficacy of oral SERDS versus standard of care (SOC) ET according to ESR1 mutation (ESR1mut). We searched PubMed, Embase, Cochrane, Web of Science, and congresses websites (ASCO and ESMO) for RCTs including HR+/HER2 negative aBC patients who were randomized to oral SERDs versus SOC ET and had received at least one prior line of ET. Outcomes of interest were progression-free survival (PFS) according to ESR1mut and objective response (ORR). Heterogeneity was evaluated with I2 statistics, and random-effect models were fitted. Of 519 studies screened, four RCTs were included, comprising 1290 patients, of whom; 41% harbored ESR1mut, 92.5% were postmenopausal, and 66% had visceral metastasis. Prior treatment for aBC included iCDK4/6 (73.3%), chemotherapy (21.6%), and; 25.2% of patients received ≥ two prior ET. In a pooled analysis, oral SERDs significantly improved PFS compared to SOC ET (HR 0.76 [95% CI 0.63-0.93 p=0.007]). ESR1mut patients treated with SERDs achieved a significant prolonged PFS than SOC ET (HR 0.59 [95% CI 0.45-0.77 p<0.001]), which was not seen in the wild-type ESR1 group (HR 0.84 [95%CI 0.67-1.04 p=0.11]). ORR was 11% in patients treated with SERDs and 7.4% in SOC ET (p=0,06).Table: 220PTrialPhaseNFollow-up Median, monthsOral SERD (N)SOC ET (N)ESR1mutBidard, 2022III47715.1Elacestrant (239)Fulvestrant, AI (238)228Oliveira, 2022II14716.6Arm 1: camizestrant 75mg (74)Fulvestrant (73)88146Arm 2: camizestrant 150mg (73)88Tonaley, 2022II290NAAmcenestrant (143)Fulvestrant, AI, tamoxifen (147)120Jimenez, 2022II3037.89Giredestrant (151)Fulvestrant, AI (152)90N: number; SERD: Selective Estrogen Receptor Degrader; SOC: standard of care; ET: endocrine therapy; mut: mutant; NA: not available. Open table in a new tab N: number; SERD: Selective Estrogen Receptor Degrader; SOC: standard of care; ET: endocrine therapy; mut: mutant; NA: not available. Oral SERDs improved PFS compared to SOC ET for ESR1mut HR+/HER2 negative metastatic breast cancer, but not for unselected patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.040 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".