The Landscape of Genomic Alterations in Receptor Tyrosine Kinase Pathways in Biliary Cancers: Implications for Targeted Therapies
Bibliographic record
Abstract
BACKGROUND: Biliary carcinomas are aggressive cancers with a high mortality rate. When metastatic, biliary cancers are associated with a short survival and low response to treatments. The first line therapy of metastatic biliary carcinomas consists of a platinum doublet chemotherapy combination with an immune checkpoint inhibitor and results in a median overall survival in the range of approximately 12-13 months, with 20% to 25% of patients surviving at 2 years. Second line chemotherapy options based on fluoropyrimidines are associated with a median survival of less than 6 months. Genomic studies in recent years have clarified molecular aspects of biliary cancers and have confirmed the molecular heterogeneity between the intrahepatic, extrahepatic and gallbladder primary sites. METHODS: Publicly available genomic cohorts of biliary cancer primary locations were interrogated for common mutations and copy number alterations with a focus on receptor tyrosine kinases and their signal transduction pathways. RESULTS: Specific mutations and structural alterations have different prevalence depending on the primary location. Alterations in receptor tyrosine kinases and the transduction pathways originating from them show differential prevalence in the primary locations of the biliary cancers and create diverse treatment opportunities that can be harnessed for drug development. Approximately 49% of intrahepatic, 57.6% of gallbladder, and 66% of extrahepatic carcinomas harbor RTK pathway alterations. CONCLUSIONS: Targeted therapies for individual components of these kinase receptors and pathways, including FGFR2, HER2, BRAF and others, have already been introduced in clinical practice for the treatment of patients with biliary tumors bearing alterations in these genes. The findings underscore the need for primary site-driven genomic testing to guide therapy selection. The current analysis discusses strategies to create opportunities for clinically available targeted therapies.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".