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Record W2050975232 · doi:10.1002/lt.22318

Immunosuppression and hepatocellular carcinoma

2011· review· en· W2050975232 on OpenAlexaboutno aff
Hans J. Schlitt, F. Mornex, Abraham Shaked, James F. Trotter

Bibliographic record

VenueLiver Transplantation · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsnot available
Fundersnot available
KeywordsImmunosuppressionSirolimusHepatocellular carcinomaCalcineurinMedicineTacrolimusLiver transplantationCancer researchPI3K/AKT/mTOR pathwayTransplantationPharmacologyImmunologyInternal medicineSignal transductionBiology

Abstract

fetched live from OpenAlex

Immunosuppressive medications inhibit the tumor surveillance properties of the immune system. Consequently, immunosuppression may increase the likelihood of hepatocellular carcinoma (HCC) recurrence after liver transplantation. However, most data on the effects of immunosuppression on HCC recurrence have been derived from experimental studies (in vitro and animal studies) and from nonrandomized, single-center reports. No randomized trials evaluating the effects of immunosuppressive agents on HCC recurrence after liver transplantation have been published. Therefore, the precise effects of immunosuppression on HCC recurrence in liver transplant recipients are difficult to assess. CNI, calcineurin inhibitor; HCC, hepatocellular carcinoma; mTOR, mammalian target of rapamycin; SIR, sirolimus; TAC, tacrolimus. Considerable experimental evidence from in vitro and animal studies shows that except for mammalian target of rapamycin (mTOR) inhibitors, immunosuppressive agents increase the malignant behavior of HCC. At the cellular level, the calcineurin inhibitors (CNIs) tacrolimus (TAC) and cyclosporine A enhance the cellular growth of malignant cells (including hepatoma). Cyclosporine A promotes tumor growth by enhancing cancer cell invasiveness1 and by inhibiting DNA repair.2 TAC increases hepatoma cell proliferation in animal models.3 In humans, CNI administration is associated with a significant reduction in the doubling time for HCC (from 273 to 37 days).4 However, mTOR inhibitors may have beneficial effects in patients with malignancies such as HCC. mTOR is overexpressed by up to two-thirds of HCCs, and this makes inhibition of this pathway a potential therapeutic target.5, 6 Sirolimus (SIR) inhibits hepatoma cell proliferation in vitro and down-regulates vascular endothelial growth factor expression.7, 8 In animal models, rats treated with SIR experienced significantly longer survival and developed smaller tumors and fewer extrahepatic metastases in comparison with controls.9 The only class of immunosuppressive agents that may reduce the recurrence of HCC is mTOR inhibitors. There are several case reports describing improvements in patients with extensive HCC who were converted to SIR.10-12 In addition, numerous single-center studies have found a statistically significant benefit related to HCC recurrence in patients receiving SIR from the time of transplantation (see Table 1). However, because none of these studies was randomized, there was a significant potential for selection, treatment, or reporting bias toward more positive findings for SIR. In a study from Dallas, 106 HCC patients received TAC and mycophenolate mofetil, and 121 received SIR.13 Patients in the SIR group had significantly higher recurrence-free survival rates in comparison with patients in the TAC group (P = 0.0003). In addition, the administration of SIR versus TAC was associated with significantly higher patient survival rates at 1 year (94% versus 79%), at 3 years (85% versus 66%), and at 5 years (80% versus 59%, P = 0.001). The absence of SIR was independently associated with death by a multivariate stepwise Cox regression analysis (hazard ratio = 3.0, 95% confidence interval = 1.6-5.6, P < 0.0002). Investigators from Bologna conducted a matched cohort study of 31 transplant patients with HCC who were receiving SIR and 31 controls who were receiving TAC.14 The SIR patients had a significantly higher 3-year recurrence-free survival rate (86%) in comparison with the TAC patients (56%, P = 0.04). Similar data were reported by researchers from Denver, who compared the outcomes of 45 SIR patients and 52 contemporaneous controls.15 At 5 years, the HCC recurrence-free survival rate was 79% for SIR patients and 54% for controls. The mortality/HCC recurrence risk ratio (SIR/CNI) was 0.622 (95% confidence interval = 0.385-0.954, P < 0.03). Finally, the Edmonton group reported the outcomes of 70 transplant patients with HCC who were receiving SIR and a low-dose CNI; 34 were within the Milan criteria, and 36 were beyond the criteria.16 They found that after a median follow-up interval of 49 months, 8 patients had experienced HCC recurrence: 2 of the 34 patients (6%) within the Milan criteria and 6 of the 36 patients (17%) beyond the Milan criteria. A recent analysis of HCC outcomes from the Scientific Registry of Transplant Recipients reiterated these findings.17 The study included 2491 liver transplant recipients with HCC and 12,167 liver transplant recipients without HCC who underwent transplantation between March 2002 and March 2009. In a multivariate analysis, SIR-based maintenance therapy was associated with improved survival after transplantation for HCC (hazard ratio = 0.53, 95% confidence interval = 0.31-0.92, P ≤ 0.05). CNIs showed no impact on HCC outcomes. In conclusion, according to experimental data and uncontrolled studies, immunosuppression may have a negative effect on HCC recurrence after liver transplantation (level C). However, no randomized trials have evaluated the impact of immunosuppression on HCC recurrence. The only class of drugs that may have a differential effect on HCC recurrence is mTOR inhibitors. These agents have consistently been shown in uncontrolled, single-center studies to reduce the rate of recurrence (level C).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.273
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2011
Admission routes1
Has abstractyes

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