Molecular profiling of advanced biliary cancer: Lost in translation from bench to bedside.
Bibliographic record
Abstract
283 Background: Advanced Biliary Cancer (ABC) is a collection of diseases which carry poor prognosis, and many patients derive limited benefit from chemotherapy. Identification of molecular drivers of ABC may help to predict treatment response and direct development of targeted therapy. Methods: Formalin fixed paraffin embedded (FFPE) tissue form patients with ABC treated at Princess Margaret Cancer Centre was analysed by MassARRAY Sequenom panel (23 genes, 279 mutations), or by next general sequencing (NGS) using Proton or Illumina MiSeq TruSeq Amplicon Cancer Panel (48 genes, 212 amplicons, ≥500x coverage). Clinicopathologic and treatment data were collected from electronic health records. Results: Of 112 tested patients, 16 had insufficient DNA, and 96 had data for analysis: 13 with ampullary cancer (AC), 19 with hilar/distal bile duct (DBD), 43 with gallbladder (GBC), and 21 with intrahepatic (IHC). 13 patients had Sequenom testing, 85 had NGS with Miseq or Proton. 127 mutations were identified in 60 patients, 36 had no mutations detected: 23 in AC, 54 in GBC, 24 in IHC, 26 in DBD. The most frequent mutations were in TP53 (34%) and KRAS (20%). TP53 and SMAD4 mutations appeared most common in GBC, BRAF and KRAS mutations were most common in AC, and IDH1 and FGFR2mutations were seen only in IHC. 14 patients (15%) had a mutation for which targeted treatment could be applied. Conclusions: Profiling of patients with ABC is feasible and can identify some molecular drivers, with different tumour sites demonstrating distinct biological patterns. Only a limited number of patients are shown to have clinically relevant mutations with current NGS techniques, suggesting additional techniques (whole genome/RNA sequencing) may be required to fully characterise these diseases and identify new therapeutic targets. [Table: see text]
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".