Achieving tolerance modifies cancer susceptibility profiles in liver transplant recipients
Bibliographic record
Abstract
Long-term survival of transplant recipients is significantly impacted by malignancy. We aimed to determine whether calcineurin inhibitor (CNI)-treated recipients converted to and weaned off molecular target of rapamycin inhibitor (mTOR-I) therapy have favorable changes in their molecular profiles in regard to malignancy risk. We performed gene expression profiling from liver biopsy and blood (PBMC) specimens followed by network analysis of key dysregulated genes, associated diseases and disorders, molecular and cellular functions using IPA software. Twenty non-immune, non-viremic patients were included, and 8 of them achieved tolerance. Two comparisons were performed: (1) tolerance time point vs tacrolimus monotherapy and (2) tolerance time point vs sirolimus monotherapy. Upon achieving tolerance, IPA predicted significant activation of DNA damage response (p = 5.40e-04) and inhibition of DNA replication (p = 7.56e-03). Conversion from sirolimus to tolerance showed decrease in HCC (p = 1.30e-02), hepatic steatosis (p = 5.60e-02) and liver fibrosis (p = 2.91e-02) associated genes. In conclusion, this longitudinal study of patients eventually achieving tolerance reveals an evolving molecular profile associated with decreased cancer risk and improved hepatic steatosis and liver fibrosis. This provides a biological rationale for attempting conversion to mTOR-I therapy and tolerance following liver transplantation particularly in patients at higher risk of cancer incidence and progression post-transplant.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".