EPID-10. CNSONTRK STUDY: CLINICAL CHARACTERISTICS AND OUTCOME OF CENTRAL NERVOUS SYSTEM TUMORS HARBORING NTRK GENE FUSIONS
Bibliographic record
Abstract
Abstract BACKGROUND TRK fusions are detected in less than 2% of central nervous system (CNS) tumors. There are limited data on the clinical course of affected patients. METHODS We conducted an international retrospective cohort study of patients with TRK fusion-driven CNS tumors. Data extracted included demographics, histopathology, TRK gene fusion, treatment modalities and outcomes. The growth modulation index (GMI) was calculated as a ratio of time to progression with TRK inhibitors to time to progression with the prior line of therapy. RESULTS 119 patients with TRK fusion-driven primary CNS tumors were identified. Median age at diagnosis was 4.5 years (range 0.0–78.3); 101 (85.6%) were < 18 and 45.3% were < 3 years of age. The most frequent tumor location was hemispheric (63.8%). Tumor types included 68 high-grade gliomas (HGG; 57.1%), 33 low-grade gliomas (LGG; 27.7%), 5 embryonal tumors (4.2%) and 13 others (10.9%). Median follow-up was 38.5 months (range 0.03–229.3). Pediatric patients had a better prognosis with a median OS of 185.5 months compared to 24.8 months in adults (p< .0001). Patients with LGG also had a better outcome with median OS that was not reached for LGG compared to 99.5 months for HGG and 38.5 months for embryonal tumors (p=0.0012). Out of 16 evaluable treatment regimens with larotrectinib an objective response was reported in 11 (11/16 (68.8%)-4 CR, 5 PR and 2 MR) compared to 38.1% for non-targeted treatment regimens (24/63 (38.1%)-10 CR, 13 PR and 1 MR). The GMI was 2.11 for pediatric patients treated with larotrectinib. CONCLUSIONS We report a large cohort of patients with TRK fusion-driven primary CNS tumors. Children with LGG glioma had a favorable outcome compared to adult and HGG. Larotrectinib appears to be associated with a better response rate and longer duration of response compared to non-targeted 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".