Antiangiogenesis for Advanced Non-Small-Cell Lung Cancer in the Era of Immunotherapy and Personalized Medicine
Bibliographic record
Abstract
Over the past decade, patients with advanced non-small-cell lung cancer (NSCLC) have witnessed substantial advances in regards to therapeutic alternatives. Among newly developed agents, angiogenesis inhibitors were extensively tested in different settings and have produced some favorable outcomes despite several shortcomings. Bevacizumab is the most examined agent in this context and has demonstrated significant survival benefits when combined with standard chemotherapy in eligible patients. Preliminary results on the addition of bevacizumab to erlotinib in patients with EGFR-mutated NSCLC seem promising. Other antiangiogenic agents were also tested, but ramucirumab and nintedanib are the only agents with a positive impact on survival. More recently, immune checkpoint inhibitors (ICIs) have had considerable success due to their prolonged durations of response, yet response rates are still deemed suboptimal, and various combination therapies are being tested in an effort to improve efficacy. Preclinical evidence suggests an immunosuppressive effect of pro-angiogenic factors, which sets up a plausible rationale for combining ICIs and antiangiogenic agents. Herein, we review the landmark data supporting the success of angiogenesis inhibitors, and we discuss the potential for combination with immunotherapy and targeted agents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".