Next-generation sequencing: Profiling gallbladder cancer (GBC).
Bibliographic record
Abstract
286 Background: Molecular profiling data of GBC is scant and it is often included with other biliary cancers for analysis, which may hinder advancing drug discovery. Methods: Archival formalin fixed paraffin embedded (FFPE) tissue of GBC from 2 research hospitals in Toronto (n=21) and Hong Kong (n=21) were analyzed 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). Results of other biliary cancer and ampullary cancer from an ongoing profiling project were also reported. Results: Twelve biliary cancer samples first analyzed with Sequenom revealed no mutations. Reanalysis with NGS of these yielded mutations in 5. All subsequent samples were analyzed with NGS (n=57). Mutations were identified in 80% [53 mutations in 42 GBC, 8 mutations in 9 intrahepatic cholangiocarcinoma (IHC), 7 mutations in 6 hilar/distal bile duct cancers (DBD)]. The most frequent mutations in GBC were TP53 and SMAD4, and KRAS mutation was found in 7% (Table). PIK3CA mutation was found in 5% of GBC but not the other biliary cancers, and IDH1 mutation was exclusive for IHC, in agreement with published literature. TP53 mutations in GBC patients did not correlate with gender, tumor grade, survival, or treatment response to gemcitabine-based chemotherapy. There was no difference in mutation patterns in GBC between 2 institutions/countries. Conclusions: NGS can be utilized for molecular profiling of biliary cancer, detecting potentially actionable targets in the majority of cases. Our preliminary data suggests GBC may have its own molecular profile, deserving special consideration in trial design for biliary cancer. To our knowledge the current study is the biggest cohort of NGS analysis for GBC. [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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".