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

Ann Surg. 2022 Nov 1;276(5):868-874. doi: 10.1097/SLA.0000000000005637. Epub 2022 Aug 1.

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.

Publication types

  • Systematic Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carcinoma, Hepatocellular* / genetics
  • Carcinoma, Hepatocellular* / surgery
  • Humans
  • Liver Neoplasms* / genetics
  • Liver Neoplasms* / surgery
  • Liver Transplantation*
  • Neoplasm Recurrence, Local / genetics
  • Neoplasm Recurrence, Local / pathology
  • Patient Selection
  • RNA
  • Retrospective Studies
  • Risk Factors
  • Transcriptome

Substances

  • RNA