Dataset Information

A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation.

ABSTRACT:

Objective

To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT).

Background

Liver cancer is one of the most frequent causes of cancer-related mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. In addition, clinical criteria widely applied in patient eligibility decisions miss potentially curable patients while selecting patients that relapse after transplantation.

Methods

A literature systematic review singled out candidate biomarkers whose RNA levels were assessed by quantitative PCR in tumor tissue from 138 HCC patients submitted to LT (>5 years follow up, 32% beyond Milan criteria). The resulting 4 gene signature was combined with clinical variables to develop a decision algorithm using machine learning approaches. The method was named HepatoPredict.

Results

HepatoPredict identifies 99% disease-free patients (>5 year) from a retrospective cohort, including many outside clinical criteria (16%-24%), thus reducing the false negative rate. This increased sensitivity is accompanied by an increased positive predictive value (88.5%-94.4%) without any loss of long-term overall survival or recurrence rates for patients deemed eligible by HepatoPredict; those deemed ineligible display marked reduction of survival and increased recurrence in the short and long term.

Conclusions

HepatoPredict outperforms conventional clinical-pathologic selection criteria (Milan, UCSF), providing superior prognostic information. Accurately identifying which patients most likely benefit from LT enables an objective stratification of waiting lists and information-based allocation of optimal versus suboptimal organs.

SUBMITTER: Pinto-Marques H

PROVIDER: S-EPMC9534058 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation.

Pinto-Marques Hugo H Cardoso Joana J Silva Sílvia S Neto João L JL Gonçalves-Reis Maria M Proença Daniela D Mesquita Marta M Manso André A Carapeta Sara S Sobral Mafalda M Figueiredo Antonio A Rodrigues Clara C Milheiro Adelaide A Carvalho Ana A Perdigoto Rui R Barroso Eduardo E Pereira-Leal José B JB

Annals of surgery 20220801 5

<h4>Objective</h4>To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT).<h4>Background</h4>Liver cancer is one of the most frequent causes of cancer-related mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. In addition, clinical criteria widely applied in patient eligibility decisions miss p ...[more]

PMID: 35916378

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Project description:Background and aims Liver transplantation (LT) can be offered to patients with Hepatocellular carcinoma (HCC) beyond Milan criteria. However, there are currently limited molecular markers on HCC explant histology to predict recurrence, which arises in up to 20% of LT recipients. The goal of our study was to derive a combined proteomic/transcriptomic signature on HCC explant predictive of recurrence post-transplant using unbiased, high-throughput approaches. Methods Patients who received a LT for HCC beyond Milan criteria in the context of hepatitis B cirrhosis were identified. Tumor explants from patients with post-transplant HCC recurrence (N = 7) versus those without recurrence (N = 4) were analyzed by mass spectrometry and gene expression array. Univariate analysis was used to generate a combined proteomic/transcriptomic signature linked to recurrence. Significantly predictive genes and proteins were verified and internally validated by immunoblotting and immunohistochemistry. Results Seventy-nine proteins and 636 genes were significantly differentially expressed in HCC tumors with subsequent recurrence (p < 0.05). Univariate survival analysis identified Aldehyde Dehydrogenase 1 Family Member A1 (ALDH1A1) gene (HR = 0.084, 95%CI 0.01–0.68, p = 0.0152), ALDH1A1 protein (HR = 0.039, 95%CI 0.16–0.91, p = 0.03), Galectin 3 Binding Protein (LGALS3BP) gene (HR = 7.14, 95%CI 1.20–432.96, p = 0.03), LGALS3BP protein (HR = 2.6, 95%CI 1.1–6.1, p = 0.036), Galectin 3 (LGALS3) gene (HR = 2.89, 95%CI 1.01–8.3, p = 0.049) and LGALS3 protein (HR = 2.6, 95%CI 1.2–5.5, p = 0.015) as key dysregulated analytes in recurrent HCC. In concordance with our proteome findings, HCC recurrence was linked to decreased ALDH1A1 and increased LGALS3 protein expression by Western Blot. LGALS3BP protein expression was validated in 29 independent HCC samples. Conclusions Significantly increased LGALS3 and LGALS3BP gene and protein expression on explant were associated with post-transplant recurrence, whereas increased ALDH1A1 was associated with absence of recurrence in patients transplanted for HCC beyond Milan criteria. This combined proteomic/transcriptomic signature could help in predicting HCC recurrence risk and guide post-transplant surveillance. Supplementary Information The online version contains supplementary material available at 10.1186/s12014-021-09333-x.

Dataset Information

A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation.

Objective

Background

Methods

Results

Conclusions

Publications

A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation.

Similar Datasets

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