Lucitanib for the Treatment of HR+/HER2− Metastatic Breast Cancer: Results from the Multicohort Phase II FINESSE Study
Bibliographic record
Abstract
Abstract Purpose: The FGFR1 gene is amplified in 14% of patients with HR+/HER2− breast cancer. Efficacy and safety of lucitanib, an inhibitor of VEGFR1-3, FGFR1-3, and PDGFRα/β, were assessed. Patients and Methods: Patients with HR+/HER2− metastatic breast cancer (MBC) received oral lucitanib in three centrally confirmed cohorts: (i) FGFR1 amplified, (ii) FGFR1 nonamplified, 11q13 amplified, and (iii) FGFR1 and 11q13 nonamplified. Key inclusion criteria included Eastern Cooperative Oncology Group Performance Status ≤2, ≥1 line of anticancer therapy, but ≤2 lines of chemotherapy. Primary endpoint was overall response rates (ORR) by RECIST1.1. Simon's two-stage design was used: If ≥2 patients responded among 21 patients, 20 additional patients could be enrolled in each cohort. FGFR1 copy-number variation (CNV) was determined by FISH and droplet digital PCR, whereas FGFR1 expression was determined by IHC. Results: Seventy-six patients (32/18/26 in cohorts 1/2/3) from nine countries were enrolled. The prespecified primary endpoint was met in cohort 1 with ORR of 19% [95% confidence interval (CI), 9%–35%], but not in cohorts 2 and 3 with ORR of 0% (95% CI, 0%–18%) and 15% (95% CI, 6%–34%), respectively. Frequent adverse events included hypertension (87%), hypothyroidism (45%), nausea (33%), and proteinuria (32%). Exploratory biomarker analyses suggested higher ORR in patients with high FGFR1 amplification (≥4 CNV) than those without high amplification (22% vs. 9%). ORR in patients with FGFR1-high tumors (IHC, H-score ≥50) was 25% versus 8% in FGFR1-low cancers. Conclusions: Lucitanib had modest antitumor activity and significant hypertension-related toxicity in patients with HR+/HER2− MBC. Although based on small sample sizes, exploratory biomarker analyses suggested that patients with high FGFR1 amplification or expression might derive greater benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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 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".