Tepotinib Efficacy in a Patient with Non-Small Cell Lung Cancer with Brain Metastasis Harboring an <i>HLA-DRB1-MET</i> Gene Fusion
Bibliographic record
Abstract
Alterations in c-MET, a tyrosine kinase receptor encoded by the MET gene, have been reported in approximately 3% of non-small cell lung cancer (NSCLC) cases and carry important treatment implications. The best studied genetic alterations are exon 14 skipping and gene amplification; however, gene rearrangement has also been described, and multiple fusion partners have been reported. Recently, in METex14-mutated NSCLC, multitarget tyrosine kinase inhibitors (TKIs), such as crizotinib and cabozantinib, as well as MET-selective TKIs, such as tepotinib and capmatinib, have demonstrated durable responses. In this study, we present the case of a 41-year-old woman with advanced NSCLC harboring an HLA-DRB1-MET gene fusion. The patient was offered successively two different MET multikinase inhibitors, crizotinib and cabozantinib, and the selective inhibitor tepotinib. Each time, including under tepotinib, the patient experienced rapid and complete responses associated with a tremendous improvement in her physical function. KEY POINTS: To our knowledge, this is the first report of a patient with non-small cell lung cancer harboring an HLA-DRB1-MET gene fusion demonstrating a clinical response to multiple MET inhibitors, including tepotinib. This finding illustrates the efficacy and rationale to targeting MET regardless of fusion partner and gives insight to pooling of patients with different MET fusion products in trials assessing safety and efficacy of novel molecules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".