A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation. Issue 5 (1st November 2022)

• Record Type: Journal Article
• Authors: 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 B.
• Is Part Of: Annals of surgery. Volume 276:Issue 5(2022)
• Page Start: 868
• Page End: 874
• Publication Date: 2022-11-01
• Subjects: hepatocellular carcinoma -- liver transplant -- gene expression signature -- prognostic -- algorithm; Surgery -- Periodicals; 617.005
• DOI: 10.1097/SLA.0000000000005637
• Languages: English
• ISSNs: 0003-4932

Abstract

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.