EPCT-08. ACTIVITY OF LAROTRECTINIB IN PEDIATRIC TROPOMYOSIN RECEPTOR KINASE (TRK) FUSION CANCER PATIENTS WITH PRIMARY CENTRAL NERVOUS SYSTEM (CNS) TUMORS
Bibliographic record
Abstract
Abstract BACKGROUND TRK fusions are oncogenic drivers in a variety of tumors, many involving the CNS. Larotrectinib, a selective FDA- and EMA-approved TRK inhibitor, demonstrated a 79% objective response rate (ORR) and a 35.2-month median duration of response (DoR) in adult and pediatric patients with various non-CNS solid tumors harboring NTRK gene fusions. We report the clinical activity of larotrectinib in pediatric patients with primary TRK fusion CNS tumors. METHODS Patients aged <18 years with primary CNS tumors harboring an NTRK gene fusion detected by local molecular testing who were treated with larotrectinib in two clinical trials (NCT02637687, NCT02576431) were identified. Larotrectinib was administered until disease progression, withdrawal, or unacceptable toxicity. Disease status was investigator assessed (RANO). RESULTS As of February 2019, 14 pediatric patients with primary TRK fusion CNS tumors were identified. Gene fusions involved NTRK2 (n=10), NTRK1 (n=2), and NTRK3 (n=2). Median age was 7.0 years (range 1.3–16.7). ORR was 45% (95% CI 17–77%) among 11 evaluable patients. Two patients had complete responses (pending confirmation), three had confirmed partial responses, and six had stable disease. 24-week disease control rate was 73%. DoR ranged from 2.6+ to 5.5+ months and progression-free survival ranged from 0.03+ to 13.9+ months. Duration of treatment ranged from 0.03+ to 16.6+ months. Treatment-emergent adverse events were mainly grade 1–2. CONCLUSIONS Larotrectinib resulted in objective responses and durable disease control in pediatric patients with primary TRK fusion CNS tumors. These results support expanded testing for NTRK gene fusions in patients with 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 | 0.000 |
| 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) | 0.003 | 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".