Clinical experience with erlotinib in non-small-cell lung cancer(NSCLC)
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death worldwide. Despite the introduction of more- effective chemotherapeutic agents, it appears that a survival plateau has been reached, so new treatment strategies are clearly needed. One innovative therapeutic cancer strategy is the introduction of biological agents that target specific intracellular pathways related to the distinctive properties of cancer cells. Among these agents, epidermal growth factor receptor (EGFR)-targeting agents have received particular attention in lung cancer. Numerous EGFR blockers have been evaluated, including monoclonal antibodies to the receptor and small-molecule tyrosine kinase inhibitors. The present review focuses on the tyrosine kinase inhibitor erlotinib. Preclinical studies have shown that erlotinib blocks the growth of human non-small-cell lung cancer (NSCLC) cell lines in vitro by inhibiting the receptor and the downstream protein phosphorylation. In a randomized study conducted by the National Cancer Institute of Canada (BR.21) in second- and third-line NSCLC treatment, erlotinib significantly prolonged overall survival and decreased symptoms compared with placebo. A crucial aspect of the clinical development of molecular-targeted therapies is to understand which patients will obtain clinical benefit from their use. Sensitivity to erlotinib has been associated with EGFR mutations, most commonly deletions of four to six amino acids in exon 19 or a point mutation (L858R) in exon 21. Increased EGFR gene copy number has also been pointed out as a good predictive marker for erlotinib response. Intense research activity is ongoing to validate known predictive markers and to discover new tools which maximize clinical benefit using erlotinib. However, there is no conclusive evidence, as yet, linking response to survival.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".