mTOR Inhibition Is Most Beneficial After Liver Transplantation for Hepatocellular Carcinoma in Patients With Active Tumors
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the survival benefit of sirolimus in patients undergoing liver transplantation (LT) for hepatocellular carcinoma (HCC) (exploratory analysis of the SiLVER-trial). SUMMARY AND BACKGROUND DATA: Patients receiving LT) for HCC are at a high risk for tumor recurrence. Calcineurin inhibitors have shown evidence to promote cancer growth, whereas mammalian target of rapamycin (mTOR) inhibitors like sirolimus have anticancer effects. In the SiLVER-trial (Clinicaltrials.gov: NCT00355862), the effect of sirolimus on the recurrence of HCC after LT was investigated in a prospective randomized trial. Although the primary endpoint of improved disease-free survival (DFS) with sirolimus was not met, outcomes were improved for patients in the sirolimus-treatment arm in the first 3 to 5 years. To learn more about the key variables, a multivariate analysis was performed on the SiLVER-trial data. PATIENTS AND METHODS: Data from 508 patients of the intention-to-treat analysis were included in exploratory univariate and multivariate models for overall survival (OS), DFS and a competing risk analysis for HCC recurrence. RESULTS: Sirolimus use for ≥3 months after LT for HCC independently reduced the hazard for death in the multivariate analysis [hazard ratio (HR): 0.7 (95% confidence interval, CI: 0.52-0.96, P = 0.02). Most strikingly, patients with an alpha-fetoprotein (AFP) ≥10 ng/mL and having used sirolimus for ≥3 months, benefited most with regard to OS, DFS, and HCC-recurrence (HR: 0.49-0.59, P = 0.0079-0.0245). CONCLUSIONS: mTOR-inhibitor treatment with sirolimus for ≥3 months improves outcomes in LT for HCC, especially in patients with AFP-evidence of higher tumor activity, advocating particularly for mTOR inhibitor use in this subgroup of patients. CLINICAL TRIAL REGISTRATION: EudraCT: 2005-005362-36 CLINICALTRIALS.GOV:: NCT00355862.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".