Prevalence of NTRK Fusions in Canadian Solid Tumour Cancer Patients
Bibliographic record
Abstract
INTRODUCTION: Neurotrophic tyrosine receptor kinase (NTRK) gene fusions occur in ~ 0.3% of all solid tumours but are enriched in some rare tumour types. Tropomyosin receptor kinase (TRK) inhibitors larotrectinib and entrectinib are approved as tumour-agnostic therapies for solid tumours harbouring NTRK fusions. METHODS: This study investigated the prevalence of NTRK fusions in Canadian patients and also aimed to help guide NTRK testing paradigms through analysis of data reported from a national clinical diagnostic testing program between September 2019 and July 2021. RESULTS: Of 1,687 patients included in the final analysis, NTRK fusions were detected in 0.71% (n = 12) of patients representing salivary gland carcinoma (n = 3), soft tissue sarcoma (n = 3), CNS (n = 3), and one in each of melanoma, lung, and colorectal cancer. All three salivary gland carcinomas contained ETV6-NTRK3 fusions. Thirteen (0.77%) clinically actionable incidental findings were also detected. Two of the 13 samples containing incidental findings were NTRK fusion-positive (GFOD1-NTRK2, FGFR3-TACC3 in a glioblastoma and AFAP1-NTRK2, BRAF c.1799T>A in a glioma). The testing algorithm screened most patient samples via pan-TRK immunohistochemistry (IHC), whereas samples from the central nervous system (CNS), pathognomonic cancers, and confirmed/ putative NTRK fusion-positive samples identified under research protocols were reflexed straight to next-generation sequencing (NGS). CONCLUSION: These findings highlight the benefit and practicality of a diagnostic testing program to identify patients suitable for tumour-agnostic TRK inhibitor therapies, as well as other targeted therapies, due to clinically actionable incidental findings identified. Collectively, these findings may inform future guidance on selecting the appropriate testing approach per tumour type and on optimal NTRK testing algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".