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

Hugo Pinto-Marques, Joana Cardoso, Sílvia Silva, João L Neto, Maria Gonçalves-Reis, Daniela Proença, Marta Mesquita, André Manso, Sara Carapeta, Mafalda Sobral, Antonio Figueiredo, Clara Rodrigues, Adelaide Milheiro, Ana Carvalho, Rui Perdigoto, Eduardo Barroso, José B Pereira-Leal

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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).

SUMMARY BACKGROUND DATA: 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. Additionally, 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 y follow up, 32% beyond Milan criteria). The resulting four 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 y) 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.

Original languageEnglish
Pages (from-to)868 - 874
JournalAnnals of surgery
Volume276
Issue number5
Early online date1 Aug 2022
DOIs
Publication statusPublished - 1 Nov 2022

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