Comprehensive Biomarker Analysis and Final Efficacy Results of Sorafenib in the BATTLE Trial
Bibliographic record
Abstract
PURPOSE: To report the clinical efficacy of sorafenib and to evaluate biomarkers associated with sorafenib clinical benefit in the BATTLE (Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination) program. PATIENTS AND METHODS: Patients with previously treated non-small cell lung cancer (NSCLC) received sorafenib until progression or unacceptable toxicity. Eight-week disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were assessed. Prespecified biomarkers included K-RAS, EGFR, and B-RAF mutations, and EGFR gene copy number. Gene expression profiles from NSCLC cell lines and patient tumor biopsies with wild-type EGFR were used to develop a sorafenib sensitivity signature (SSS). RESULTS: A total of 105 patients were eligible and randomized to receive sorafenib. Among 98 patients evaluable for eight-week DCR, the observed DCR was 58.2%. The median PFS and OS were 2.83 [95% confidence interval (CI), 2.04-3.58] and 8.48 months (95% CI, 5.78-10.97), respectively. Eight-week DCR was higher in patients with wild-type EGFR than patients with EGFR mutation (P = 0.012), and in patients with EGFR gene copy number gain (FISH-positive) versus patients FISH-negative (P = 0.048). In wild-type EGFR tumors, the SSS was associated with improved PFS (median PFS 3.61 months in high SSS vs. 1.84 months in low SSS; P = 0.026) but not with eight-week DCR. Increased expression of fibroblast growth factor-1, NF-κB, and hypoxia pathways were identified potential drivers of sorafenib resistance. CONCLUSION: Sorafenib demonstrates clinical activity in NSCLC, especially with wild-type EGFR. SSS was associated with improved PFS. These data identify subgroups that may derive clinical benefit from sorafenib and merit investigation in future trials.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".