Programmed Cell Death Protein 1 Inhibitors and MET Targeted Therapies in NSCLC With MET Exon 14 Skipping Mutations: Efficacy and Toxicity as Sequential Therapies
Bibliographic record
Abstract
Introduction NSCLC with MET exon 14 skipping mutation ( MET ex14) is associated with poor outcomes. Integration of novel targeted therapies is challenging because of barriers in testing and drug access. We, therefore, sought to characterize the treatment patterns, outcomes, and emerging issues of treatment sequencing in patients with MET ex14-mutant NSCLC. Methods We reviewed all NSCLC cases with MET ex14 alterations between 2014 and 2020 across four Canadian cancer centers. Demographics, disease characteristics, systemic therapy, overall response rates (ORRs), survival, and toxicity were summarized. Results Among 64 patients with MET ex14-mutant NSCLC, the median overall survival was 23.1 months: 127.0 months in stage 1, 27.3 months in resected stage 2 and 3, and 16.6 months in unresectable stage 3 or 4 disease, respectively. In patients with advanced disease, 22% were too unwell for systemic treatment. MET tyrosine kinase inhibitors (TKIs) were administered to 28 patients with an ORR of 33%, median progression-free survival of 2.7 months, and 3.8 months for selective TKIs. Programmed cell death protein-1 (PD-1) inhibitors were given to 25 patients—the ORR was 44% and progression-free survival was 10.6 months. No responses were seen with subsequent MET TKIs after initial TKI treatment. Grade 3 or higher toxicities occurred in 64% of patients who received MET TKI after PD-1 inhibitors versus 8% in those who did not receive PD-1 inhibitors. Conclusions Many patients with advanced MET ex14 NSCLC were too unwell to receive treatment. PD-1 inhibitors seem effective as an initial treatment, although greater toxicity was seen with subsequent MET TKIs. Thus, timely testing for MET ex14 skipping and initial therapy are imperative to improving patient 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".