Crizotinib Inhibition of ROS1-Positive Tumours in Advanced Non-Small-Cell Lung Cancer: A Canadian Perspective
Bibliographic record
Abstract
The ROS1 kinase is an oncogenic driver in non-small-cell lung cancer (NSCLC). Fusion events involving the ROS1 gene are found in 1%–2% of NSCLC patients and lead to deregulation of a tyrosine kinase–mediated multi-use intracellular signalling pathway, which then promotes the growth, proliferation, and progression of tumour cells. ROS1 fusion is a distinct molecular subtype of NSCLC, found independently of other recognized driver mutations, and it is predominantly identified in younger patients (<50 years of age), women, never-smokers, and patients with adenocarcinoma histology. Targeted inhibition of the aberrant ROS1 kinase with crizotinib is associated with increased progression-free survival (PFS) and improved quality-of-life measures. As the sole approved treatment for ROS1-rearranged NSCLC, crizotinib has been demonstrated, through a variety of clinical trials and retrospective analyses, to be a safe, effective, well-tolerated, and appropriate treatment for patients having the ROS1 rearrangement. Canadian physicians endorse current guidelines which recommend that all patients with nonsquamous advanced NSCLC, regardless of clinical characteristics, be tested for ROS1 rearrangement. Future integration of multigene testing panels into the standard of care could allow for efficient and cost-effective comprehensive testing of all patients with advanced nsclc. If a ROS1 rearrangement is found, treatment with crizotinib, preferably in the first-line setting, constitutes the standard of care, with other treatment options being investigated, as appropriate, should resistance to crizotinib develop.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".