The impact of molecular alterations in patients with advanced biliary tract cancer receiving cisplatin, gemcitabine, and durvalumab: a large, real-life, worldwide population
Bibliographic record
Abstract
BACKGROUND: Cisplatin, gemcitabine, and durvalumab combination is a standard first-line treatment for advanced biliary tract cancer. This study aimed to assess the impact of genetic alterations on outcomes in patients with advanced biliary tract cancer treated with cisplatin, gemcitabine, and durvalumab in real-world clinical practice. METHODS: Patients with unresectable, locally advanced, or metastatic biliary tract cancer treated with cisplatin and gemcitabine plus durvalumab across 39 centers in 11 countries in Europe, the United States, and Asia were included in this analysis. RESULTS: The cohort included 513 patients with advanced biliary tract cancer. The 5 most frequently altered genes were TP53 (22.1%), KRAS (13.7%), CDKN2A/B (13.6%), ARID1A (12.2%), and IDH1 (9.2%). In multivariate analysis, SMAD4 mutations were associated with improved progression-free survival (PFS) (hazard ratio [HR] = 0.49, P = .018) and overall survival (HR = 0.11, P = .023), while TP53 mutations were linked to worse PFS (HR = 1.62, P = .0047) and TERT mutations to worse overall survival (HR = 8.92, P = .0012). No other genomic alterations were statistically associated with outcomes. Subgroup analysis showed that TP53 mutations negatively affected PFS and overall survival in intrahepatic cholangiocarcinoma, while KRAS mutations were associated with poorer PFS in extrahepatic cholangiocarcinoma. No gene alterations were linked to outcomes in gallbladder cancer. CONCLUSIONS: This large-scale analysis, with comprehensive molecular profiling, supports the positive prognostic impact of SMAD4 mutations for PFS and overall survival and highlights the negative prognostic roles of TP53 (PFS) and TERT (overall survival) mutations, providing valuable insights for personalized treatment strategies in biliary tract cancer.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".