Canadian Consensus for Biomarker Testing and Treatment of TRK Fusion Cancer in Adults
Bibliographic record
Abstract
The tyrosine receptor kinase (TRK) inhibitors larotrectinib and entrectinib were recently approved in Canada for the treatment of solid tumours harbouring neurotrophic tyrosine receptor kinase (NTRK) gene fusions. These NTRK gene fusions are oncogenic drivers found in most tumour types at a low frequency (<5%), and at a higher frequency (>80%) in a small number of rare tumours (e.g., secretory carcinoma of the salivary gland and of the breast). They are generally mutually exclusive of other common oncogenic drivers. Larotrectinib and entrectinib have demonstrated impressive overall response rates and tolerability in Phase I/II trials in patients with TRK fusion cancer with no other effective treatment options. Given the low frequency of TRK fusion cancer and the heterogeneous molecular testing landscape in Canada, identifying and optimally managing such patients represents a new challenge. We provide a Canadian consensus on when and how to test for NTRK gene fusions and when to consider treatment with a TRK inhibitor. We focus on five tumour types: thyroid carcinoma, colorectal carcinoma, non-small cell lung carcinoma, soft tissue sarcoma, and salivary gland carcinoma. Based on the probability of the tumour harbouring an NTRK gene fusion, we also suggest a tumour-agnostic consensus for NTRK gene fusion testing and treatment. We recommend considering a TRK inhibitor in all patients with TRK fusion cancer with no other effective treatment options.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".