Clinicogenomic Characterization of Primary Sclerosing Cholangitis–Associated Biliary Tract Cancers
Bibliographic record
Abstract
PURPOSE: Biliary tract cancer (BTC) is the leading cause of death in patients with primary sclerosing cholangitis (PSC). PSC-related BTC is poorly understood, and the risks and benefits of conventional and immunotherapy treatments are unknown. We aimed to characterize clinical outcomes and genomes of PSC-related BTCs. EXPERIMENTAL DESIGN: This was a retrospective cohort study of patients with BTC with underlying PSC treated at MD Anderson Cancer Center (N = 46) and Princess Margaret Cancer Centre (N = 16), which were contrasted to patients with non-PSC-related BTC (N = 146). We compared outcomes between PSC and non-PSC, and PSC treated with and without immunotherapy. A combination of targeted sequencing (N = 139), whole-genome sequencing (WGS; N = 27), and WGS with paired RNA sequencing (N = 33) delineated the genomic and transcriptomic landscape of PSC-associated BTCs. RESULTS: In PSC-related BTC, the addition of immunotherapy to chemotherapy was associated with improved first-line progression-free survival (PFS; N = 22 vs. 11; median PFS, 12.2 vs. 4.7 months; P = 0.01). Immune-related adverse events were rare (N = 2, 12.5%) and improved after treatment discontinuation. Classic actionable genomic alterations, including IDH1 mutations and FGFR2 fusions, were absent in PSC-related BTCs. PSC tumors had a 2.6-fold higher tumor mutational burden (P = 3.28e-05) compared with non-PSC tumors. Transcriptomic profiling revealed a subset of PSC tumors displaying RNA signatures of immunotherapy response. CONCLUSIONS: Immunotherapy in PSC-associated BTCs seemed safe, with a potential signal of effectiveness. Given the sample size and retrospective design, these results are hypothesis-generating. Together, these results demonstrate the unique biology underlying PSC-associated BTCs, highlighting the need for prospective trials and the development of specialized treatment strategies.
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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 | 0.000 | 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 | 0.000 |
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".