RARE-45. ACTIVITY OF LAROTRECTINIB IN TRK FUSION CANCER PATIENTS WITH PRIMARY CENTRAL NERVOUS SYSTEM TUMORS
Bibliographic record
Abstract
Abstract BACKGROUND TRK fusions are oncogenic drivers of a variety of tumors, many of which can involve the central nervous system (CNS). Larotrectinib is a selective TRK inhibitor FDA-approved for the treatment of TRK fusion cancers (Drilon et al., NEJM 2018). Here we report on the clinical activity of larotrectinib in an expanded set of TRK fusion-positive primary CNS tumors. METHODS Patients with primary CNS tumors harboring a TRK fusion treated with larotrectinib on two clinical trials (NCT02637687 and NCT02576431) were identified by local molecular testing. Larotrectinib was administered until disease progression, withdrawal, or unacceptable toxicity. Disease status was investigator assessed (RANO). Data cutoff: February 19, 2019. RESULTS 18 patients with various histological types of glial tumors (11 high-grade, 4 low-grade, 3 unknown) were identified. The patients had gene fusions involving NTRK2 (n=13), NTRK1 (n=2) and NTRK3 (n=2); one was not determined. Median age was 10 years (range 1–79); 14 patients were pediatric (< 18). In 14 evaluable patients, the objective response rate was 36% (2 CR, 3 PR), with responses seen in high- and low-grade disease and across histologies. Nine patients had SD. The 24-week disease control rate was 71%. The duration of treatment ranged from 0.03+ to 16.6+ months. One patient (3.7 years old) with glioblastoma progressed after 5.5 months on larotrectinib. Sequencing revealed a solvent front mutation and the patient was subsequently enrolled in compassionate use protocol for BAY2731954 (formerly known as LOXO-195). CONCLUSION Larotrectinib is active in patients with TRK fusion cancer with intracranial disease. Confirmed responses and durable disease control were seen in primary CNS tumors of various grades and histologies. These results further support expanded testing for NTRK gene fusions in patients with 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".