Larotrectinib Compared With Real-World Non–Tropomyosin Receptor Kinase Inhibitor Therapies in Patients With Tropomyosin Receptor Kinase Fusion Cancer
Bibliographic record
Abstract
PURPOSE Neurotrophic tyrosine receptor kinase gene fusions are oncogenic drivers of various solid tumors. Larotrectinib is a highly selective tropomyosin receptor kinase (TRK) inhibitor approved for patients with TRK fusion cancer on the basis of single-arm trials. This study was a matched comparative effectiveness study of larotrectinib in clinical trials versus standard of care (SOC) in the real-world (RW) setting. METHODS Adult patients with advanced/metastatic TRK fusion non-small cell lung cancer, colorectal cancer, soft tissue sarcoma, thyroid cancer, or salivary gland carcinoma were included. Deduplicated data from RW patients were from US and ex-US data sources. Patients in the larotrectinib cohort (pooled data from three trials, ClinicalTrials.gov identifiers: NCT02122913 , NCT02576431 , and NCT02637687 ) were matched 1:1 to RW patients on the basis of tumor type and line of therapy (LOT). A propensity score (weighting) model was used to balance key characteristics between cohorts. The primary outcome was overall survival (OS). RESULTS In total, 164 patients were matched 1:1 on tumor type and LOT (82 in each cohort). Balance in the baseline covariates was achieved after weighting. Larotrectinib-treated patients had longer OS (median, not reached [NR] v 37.2 months; hazard ratio [HR], 0.44 [95% CI, 0.23 to 0.83]), time to next treatment (median, NR v 10.6 months; HR, 0.22 [95% CI, 0.13 to 0.38]), duration of therapy (median, 30.8 v 3.4 months; HR, 0.23 [95% CI, 0.15 to 0.33]), and progression-free survival (median, 36.8 v 5.2 months; HR, 0.29 [95% CI, 0.18 to 0.46]) compared with RW patients after propensity score weighting. CONCLUSION In TRK fusion cancers, treatment with larotrectinib was associated with longer OS and prolonged time to event compared with SOC in all categories measured. These RW data provide context to support larotrectinib effectiveness in this population.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".