Autor(es): Pinto-Marques, Hugo ; Cardoso, Joana ; Silva, Sílvia ; Neto, João L ; Gonçalves-Reis, Maria ; Proença, Daniela ; Mesquita, Marta ; Manso, André ; Carapeta, Sara ; Sobral, Mafalda ; Figueiredo, Antonio ; Rodrigues, Clara ; Milheiro, Adelaide ; Carvalho, Ana ; Perdigoto, Rui ; Barroso, Eduardo ; Pereira-Leal, José
Data: 2022
Identificador Persistente: http://hdl.handle.net/10362/144834
Origem: Repositório Institucional da UNL
Assunto(s): SDG 3 - Good Health and Well-being
Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.
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.
