Molecular Tumor Board-Guided Targeted Treatments for Biliary Tract Cancers in a Publicly Funded Healthcare System
Bibliographic record
Abstract
Comprehensive molecular profiling can identify alterations in biliary tract cancer (BTC) potentially treatable with targeted therapies. However, the impact of whole-genome and transcriptome sequencing (WGTS) on therapeutic decision-making in a public healthcare system is unknown. Here, BTC patients prospectively received WGTS to inform clinical care at a large Canadian academic cancer center. We characterized the proportion of targetable alterations, the treatment recommendations generated by a molecular tumor board, targeted therapies received, patient outcomes, and the financing of these treatments. A total of 55 patients with BTC prospectively underwent WGTS to inform clinical care. Of those 55, 28 (51%, 95% CI 38-64%) harbored targetable alterations. Molecular tumor boards recommended consideration of targeted therapies for 43 (78% CI: 66-87%) of 55 cases. Among the 15 patients who progressed to second-line therapy and harbored targetable alterations, 8 received nine targeted therapies. No targeted therapies were funded through the public system, and most therapies were funded through compassionate access programs from companies. These results highlight the challenges and potential for inequities when implementing precision oncology in a publicly funded healthcare system.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".