Efficacy and safety of larotrectinib in patients with TRK fusion-positive thyroid carcinoma
Bibliographic record
Abstract
© 2022 The authors.Objective: Larotrectinib is a highly selective tropomyosin receptor kinas e (TRK) inhibitor with demonstrated efficacy across various TRK fusion-positive solid tumours. We assessed t he efficacy and safety of larotrectinib in patients with TRK fusion-positive thyroid carcinoma (TC). Methods: We pooled data from three phase I/II larotrectinib clinical trials (NCT02576431, NCT02122913, and NCT02637687). The primary endpoint was the investigator-assessed objective response rate (ORR) per Response Evaluation Criteria in Solid Tumors v1.1. Secondary endpoints i ncluded duration of response (DoR), progression-free survival (PFS), overall survival (OS), and safety. Data cut-off: July 2020. Results: Twenty-nine patients (median age: 60; range: 6-80) with TRK fu sion-positive TC were treated. Tumour histology was papillary (PTC) in 20 (69%) patients, follicular (FTC) in 2 (7%), and anaplastic (ATC) in 7 (24%) patients. Among 28 evaluable patients, ORR was 71% (95% CI: 51-87); best responses were complete response in 2 (7%) patients, partial response in 18 (64%), stable disease in 4 (14%), progressive di sease in 3 (11%), and undetermined in 1 (4%) due to clinical progression prior to the first post-baseline assessment . ORR was 86% (95% CI: 64-97) for PTC/FTC and 29% (95% CI 4-71) for ATC. Median time to response was 1.87 months (range 1.64-3.68). The 24-month DoR, PFS, and OS rates were 81, 69, and 76%, respectively. Treatment-related adv erse events were mainly grades 1-2. Conclusion: In TRK fusion-positive TC, larotrectinib demonstrates rapid an d durable disease control and a favourable safety profile in patients with advanced disease requiring syste mic therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".