Canadian Perspectives: Update on Inhibition of ALK-Positive Tumours in Advanced Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Background: Inhibition of the anaplastic lymphoma kinase (ALK) oncogenic driver in advanced non-small-cell lung carcinoma (NSCLS) improves survival. In 2015, Canadian thoracic oncology specialists published a consensus guideline about the identification and treatment of ALK-positive patients, recommending use of the ALK inhibitor crizotinib in the first line. New scientific literature warrants a consensus update. Methods: Clinical trials of ALK inhibitor were reviewed to assess benefits, risks, and implications relative to current Canadian guidance in patients with ALK-positive NSCLS. Results: Randomized phase III trials have demonstrated clinical benefit for single-agent alectinib and ceritinib used in treatment-naïve patients and as second-line therapy after crizotinib. Phase II trials have demonstrated activity for single-agent brigatinib and lorlatinib in further lines of therapy. Improved responses in brain metastases were observed for all second- and next/third-generation ALK tyrosine kinase inhibitors in patients progressing on crizotinib. Canadian recommendations are therefore revised as follows: (1) Patients with advanced nonsquamous NSCLS have to be tested for the presence of an ALK rearrangement. (2) Treatment-naïve patients with ALK-positive disease should initially be offered single-agent alectinib or ceritinib, or both sequentially. (3) Crizotinib-refractory patients should be treated with single-agent alectinib or ceritinib, or both sequentially. (4) Further treatments could include single-agent brigatinib or lorlatinib, or both sequentially. (5) Patients progressing on ALK tyrosine kinase inhibitors should be considered for pemetrexed-based chemotherapy. (6) Other systemic therapies should be exhausted before immunotherapy is considered. Summary: Multiple lines of ALK inhibition are now recommended for patients with advanced NSCLS with an ALK rearrangement.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".