The complexity of finding fit-for-purpose real-world data for oncology patients with rare NTRK gene fusions and a novel solution
Bibliographic record
Abstract
Background Multiple data sources suggest low frequencies of neurotrophic tyrosine receptor kinase ( NTRK ) gene fusions across common solid tumors, ranging from 0.18% to 0.30%, making it a relevant target for a tumor-agnostic development using a single-arm basket trial. The objective of this study was to explore a multifaceted approach to building a pooled real-world dataset of patients with select solid tumors harboring NTRK1-3 gene fusions from multiple clinicogenomic databases (CGDBs) and clinical site sources. Materials and methods A novel approach was explored for identifying patients with NTRK gene fusion-positive solid tumors from real-world data (RWD) sources through existing CGDBs and global clinical site surveys. CGDBs were assessed for inclusion based on patient data, genomic testing, and accessibility of patient-level data. The clinical site surveys were used to determine the practicality of designing a chart review study where patients were identified via electronic medical records and molecular assays. Results Approximately 19% of CGDBs and 1% of the clinical sites included in the survey outreach were eligible for inclusion. Combining data from CGDBs and clinical sites through a retrospective chart review yielded a real-world cohort of 512 patients with NTRK gene fusion-positive solid tumors. Conclusion The rarity of patients with NTRK gene fusion-positive cancer and number of eligible CGDBs/clinical sites present challenges for the identification of sufficient RWD sources that can be used in comparative effectiveness studies to contextualize results from single-arm trials. This approach provides a potential solution to identify fit-for-purpose RWD to support precision medicine for patients with cancer harboring rare genomic alterations.
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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 | 0.085 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".