Exploring the Benefit/Risk Associated with Antiangiogenic Agents for the Treatment of Non–Small Cell Lung Cancer Patients
Bibliographic record
Abstract
Abstract Following the approval of bevacizumab, an antibody targeting VEGF-A, for advanced non-squamous non–small cell lung cancer (NSCLC) in 2006, intensive efforts were put into the clinical development of antiangiogenic agents for NSCLC. Currently, the other antiangiogenic agents approved for NSCLC are ramucirumab, a VEGF receptor-2 (VEGFR-2)–targeting antibody indicated for both squamous and non-squamous NSCLC in the United States, and nintedanib, an anti–VEGFR-1/2/3, platelet-derived growth factor receptor-α/β, fibroblast growth factor receptor-1/2/3 angiokinase inhibitor indicated for adenocarcinoma of the lung in the European Union. Many other antiangiogenic agents are being evaluated in phase III trials for NSCLC, including aflibercept, sunitinib, sorafenib, cediranib, and vandetanib. Although many of the same signaling pathways are targeted by these novel agents, mixed efficacy results have been observed in these trials. Moreover, safety issues have raised concerns about using antiangiogenic agents in this patient population, and fatal bleeding events have been reported. Importantly, although no biomarker has yet been validated for antiangiogenic agents in NSCLC, biomarkers that show potential include circulating levels of short VEGF-A isoforms, expression of neuropilin-1 and VEGFR-1 in tumors and plasma, genetic variants in VEGF-A and VEGFR, and tumor protein p53 mutations (with the latter having been shown to correlate with increased levels of VEGF-A transcripts). This review provides an overview of the clinical benefit and risk associated with the use of antiangiogenic agents for NSCLC, and summarizes the research to date on the identification of predictive biomarkers for antiangiogenic therapies. Clin Cancer Res; 23(5); 1137–48. ©2016 AACR.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".