First-Line Bevacizumab-Containing Therapy for Triple-Negative Breast Cancer: Analysis of 585 Patients Treated in the ATHENA Study
Bibliographic record
Abstract
BACKGROUND: The prognosis for patients with triple-negative breast cancer (TNBC) is poor and treatment options are limited. Bevacizumab improves the efficacy of standard first-line therapy in locally recurrent/metastatic breast cancer (LR/mBC). The benefit of bevacizumab seen in patients with TNBC appears similar to that observed in the overall population. We conducted an exploratory analysis of patients with TNBC treated in the single-arm routine oncology practice ATHENA study. METHODS: Patients with previously untreated LR/mBC received standard first-line chemotherapy combined with bevacizumab (10 mg/kg every 2 weeks or 15 mg/kg every 3 weeks, until progression, unacceptable toxicity, or patient/physician decision). RESULTS: Of 2,264 patients treated in ATHENA, 585 (26%) had TNBC. Most patients received single-agent taxane with bevacizumab. In the TNBC subgroup, the overall response rate was 49%, including complete responses in 10%; only 16% had primary resistant disease. Median time to progression was 7.2 months (95% CI 6.6-7.8) and median overall survival was 18.3 months (95% CI 16.4-19.7). The 1-year overall survival rate was 60%. The safety profile in TNBC was consistent with results in the overall population. CONCLUSION: This exploratory subgroup analysis suggests that first-line chemotherapy in combination with bevacizumab is an active regimen in patients with metastatic TNBC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".