New developments in the treatment of advanced squamous cell lung cancer: focus on afatinib
Bibliographic record
Abstract
Until recently, few treatment options existed for the treatment of squamous cell carcinoma (SqCC) of the lung, especially in the second-line setting following platinum-based chemotherapy. Accordingly, outcomes in this subtype of non-small-cell lung cancer (NSCLC) were generally poor. In this context, the recent availability of the checkpoint inhibitors nivolumab and pembrolizumab, the anti-VEGFR2 antibody ramucirumab (combined with docetaxel), and the ErbB-family blocker afatinib for the treatment of relapsed/refractory SqCC of the lung represent major advances. However, the rapid expansion of the treatment armamentarium invites many questions regarding optimal treatment choice and sequence in individual patients. This review focuses on the biologic rationale and clinical evidence to support the use of afatinib in this treatment setting, highlighting the prominent role of the ErbB-signaling cascade in SqCC tumors. The seminal Phase III LUX-Lung 8 study, on which the approval of afatinib is based, is discussed and contextualized with the emergence of immunotherapies. Finally, criteria are explored that might drive physicians' treatment decisions when considering the use of afatinib based on individual patient characteristics. Other ongoing developments in the treatment of SqCC of the lung that will lead to further options and welcome improvements in the management of this difficult-to-treat disease are summarized.
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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 | 0.001 |
| 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) | 0.004 | 0.002 |
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".