Efficacy and safety of larotrectinib as first-line treatment for patients with TRK fusion cancer
Bibliographic record
Abstract
BACKGROUND: Larotrectinib is a first-in-class, highly selective tropomyosin receptor kinase (TRK) inhibitor approved for tumour-agnostic use in patients with TRK fusion cancer. Data on treatment-naïve adult and paediatric patients or the subset of treatment-naïve paediatric patients who discontinued larotrectinib after surgery or achieving durable clinical benefit are unknown. MATERIALS AND METHODS: Patients with treatment-naïve (no prior systemic therapy) metastatic/unresectable TRK fusion cancer from three larotrectinib clinical trials [NCT02122913, NCT02637687 (SCOUT) and NCT02576431 (NAVIGATE)] were included. Responses were assessed by an independent review committee (RECIST v1.1). SCOUT-enrolled patients could electively discontinue larotrectinib after surgical resection of disease, or ongoing non-surgical complete/partial response (≥1 year) or stable disease (≥2 years) in a 'wait-and-see' approach. RESULTS: As of 20 July 2023, 101 patients were enrolled, with a median age of 37 years (range 0-90 years). There were 14 different tumour types; the most common were non-infantile fibrosarcoma (IFS) soft tissue sarcoma (30%), IFS (18%), salivary gland carcinoma (18%) and thyroid carcinoma (17%). The overall response rate was 77% [95% confidence interval (CI) 68% to 85%]. Median duration of response, progression-free survival and overall survival were 59 months [95% CI 33 months-not estimable (NE)], 61 months (95% CI 33 months-NE) and not reached, respectively. Twenty-five of 42 SCOUT-enrolled patients entered a 'wait-and-see' period; at the data cut-off time, the 'wait-and-see' period was ongoing in 12 of these patients. Seven of 13 patients who exited the first 'wait-and-see' period had progressive disease and resumed larotrectinib; five of these seven patients had a response to re-treatment. Most treatment-related adverse events were grade 1/2. CONCLUSIONS: Larotrectinib achieved extremely durable responses, extended survival and had a favourable safety profile in treatment-naïve patients with TRK fusion cancers, supporting its use in this population. Elective discontinuation of larotrectinib may be feasible in selected paediatric patients, with response achievable after restarting larotrectinib in cases of recurrent off-therapy disease.
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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 | 0.001 |
| 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".