CTNI-62. LONG-TERM EFFICACY AND SAFETY OF LAROTRECTINIB IN PATIENTS WITH TROPOMYOSIN RECEPTOR KINASE (TRK) FUSION PRIMARY CENTRAL NERVOUS SYSTEM (CNS) TUMORS
Bibliographic record
Abstract
Abstract BACKGROUND Larotrectinib is a highly selective TRK inhibitor that demonstrated an objective response rate (ORR) of 30% and a 24-week disease control rate (DCR) of 73% across 33 evaluable adult and pediatric patients with TRK fusion primary CNS tumors, as of July 2020 (Doz et al, Neuro Oncol 2021). We report updated data on an expanded dataset. METHODS Patients with TRK fusion primary CNS tumors in two clinical trials (NCT02637687, NCT02576431) were included. Responses were investigator-assessed. RESULTS As of July 2021, 38 patients with TRK fusion primary CNS tumors (median age, 10.8 [range 1.3–79.0] years) were identified: high-grade glioma (HGG; n = 23), low-grade glioma (LGG; n = 9), and other (n = 6). Sixteen (42%) patients had ≥ 2 prior systemic therapies. ORR for 37 evaluable patients was 30% (95% confidence interval [CI] 16–47): three complete responses, eight partial responses, 21 stable disease (16 patients ≥ 24 weeks), and five progressive disease. For pediatric patients (n = 28), the ORR was 39% (95% CI 22–59). For pediatric patients with HGG and LGG, ORRs were 43% (95% CI 18–71) and 38% (95% CI 9–76), respectively. ORRs for patients with 0, 1, 2, and ≥ 3 prior therapies were 33%, 20%, 38%, and 38%, respectively. Median time to response was 1.9 months. The 24-week DCR was 73% (95% CI 56–86). Median duration of response (DoR) was not reached; 12-month DoR rate was 64%. Median progression-free survival was 16.5 months (95% CI 6.7–not estimable). Median overall survival (OS) was not reached; 24-month OS rate was 65%. Treatment duration ranged from 0.1+ to 38.7+ months. Treatment-related adverse events (TRAEs) were mostly Grade 1–2. No patients discontinued treatment due to TRAEs. CONCLUSION Larotrectinib demonstrated a high DCR, rapid and durable responses, and a manageable safety profile in patients with TRK fusion primary CNS tumors.
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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 | 0.001 |
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".