Efficacy and safety of larotrectinib in TRK fusion-positive primary central nervous system tumors
Bibliographic record
Abstract
BACKGROUND: Larotrectinib is a first-in-class, highly selective tropomyosin receptor kinase (TRK) inhibitor approved to treat adult and pediatric patients with TRK fusion-positive cancer. The aim of this study was to evaluate the efficacy and safety of larotrectinib in patients with TRK fusion-positive primary central nervous system (CNS) tumors. METHODS: Patients with TRK fusion-positive primary CNS tumors from two clinical trials (NCT02637687, NCT02576431) were identified. The primary endpoint was investigator-assessed objective response rate (ORR). RESULTS: As of July 2020, 33 patients with TRK fusion-positive CNS tumors were identified (median age: 8.9 years; range: 1.3-79.0). The most common histologies were high-grade glioma (HGG; n = 19) and low-grade glioma (LGG; n = 8). ORR was 30% (95% confidence interval [CI]: 16-49) for all patients. The 24-week disease control rate was 73% (95% CI: 54-87). Twenty-three of 28 patients (82%) with measurable disease had tumor shrinkage. The 12-month rates for duration of response, progression-free survival, and overall survival were 75% (95% CI: 45-100), 56% (95% CI: 38-74), and 85% (95% CI: 71-99), respectively. Median time to response was 1.9 months (range 1.0-3.8 months). Duration of treatment ranged from 1.2-31.3+ months. Treatment-related adverse events were reported for 20 patients, with grade 3-4 in 3 patients. No new safety signals were identified. CONCLUSIONS: In patients with TRK fusion-positive CNS tumors, larotrectinib demonstrated rapid and durable responses, high disease control rate, and a favorable safety profile.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".