Real-World Outcomes of Elacestrant in ER+, HER2−, <i>ESR1</i> -Mutant Metastatic Breast Cancer
Bibliographic record
Abstract
PURPOSE: The EMERALD trial led to the approval of elacestrant for estrogen receptor (ER)-positive, HER2-negative, estrogen receptor 1 (ESR1)-mutated advanced or metastatic breast cancer (mBC) with disease progression following at least one line of endocrine therapy (ET). Subgroup analyses provided evidence suggesting that elacestrant enables ET sequencing in the second line before other targeted combinations, which could delay chemotherapy-based regimens. EXPERIMENTAL DESIGN: This study used claims data from the Komodo Research Dataset linked with Foundation Medicine Inc. clinical genomic data from patients with estrogen receptor+/HER2- mBC harboring an ESR1 mutation treated with elacestrant. The primary outcome measure was time to next treatment (TTNT). RESULTS: Among 306 patients, 93.8% had prior ET ± cyclin-dependent kinase 4/6 inhibitor for ≥12 months, 50.0% had prior chemotherapy, and 72.2% had prior fulvestrant. Median TTNT (mTTNT) was 8.2 months [95% confidence interval (CI), 6.3-13.0] in patients with 1 to 2 prior lines and 7.5 months (95% CI, 7.1-9.9) in those with ≥3 prior lines of ET. In patients with coexisting ESR1- and PI3K-pathway-mutated tumors, mTTNT was 6.3 months (95% CI, 4.8-7.9). mTTNT was 7.9 months (95% CI, 7.1-9.8) in all patients and was also sustained in patients with no prior fulvestrant [12.9 months (95% CI, 7.2-not reached)], no prior chemotherapy [8.4 months (95% CI, 7.1-13.3)], visceral metastasis [7.9 months (95% CI, 7.0-9.9)], and liver metastases [7.2 months (95% CI, 6.3-9.0)]. CONCLUSIONS: Elacestrant demonstrated a durable benefit in real-world clinical practice, particularly in earlier lines and in patients with prolonged prior ET exposure. Despite coexisting ESR1 and PI3K pathway mutations, TTNT remained clinically meaningful, reinforcing the role of elacestrant in personalized ET sequencing strategies prior to chemotherapy, antibody-drug conjugates, or targeted combinations. See related article by Lloyd et al., p. 169.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".