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.

FIGURE 1

HepatoPredict 2-step algorithm. A, First algorithm identifies good prognosis patients with a PPV=94% (Class I); the remaining patients go through a second algorithm that identifies further good prognosis patients at a PPV=88,5% (Class II). For the remaining patients HepatoPredict does not predict any benefit in liver transplantation. B, Patients proposed for transplantation and their outcome after 5 years according to HepatoPredict Class I and Class I+II versus Milan Criteria (Milan) or UCSF Criteria (UCSF); a positive outcome (No relapse) represented in light blue versus a negative outcome (Relapsed) in red. B, Overlap of transplanted patients that did not relapse (darker blue) with a positive prognosis according to HepatoPredict Class I or Class I+II (light blue), Milan or UCSF criteria (light gray).

FIGURE 2

Recurrence curves of transplanted liver cancer patients at 5 years (60 months), according to HepatoPredict Class I+II (blue lines) versus Milan or UCSF Criteria (gray lines, left and right columns, respectively). Cumulative recurrence curves for the entire population of transplanted patients compared with Milan (A) and UCSF (B) criteria, for the subpopulations of patients originally classified outside Milan (C) and UCSF (D) criteria, and for the subpopulations of patients originally classified within (E) Milan and (F) UCSF criteria.

FIGURE 3

Overall survival curves of transplanted liver cancer patients at ~15 years according to the original Milan or UCSF selection criteria (left and right columns, respectively). Cumulative overall survival curves for the entire population of transplanted patients divided according to (A) Milan or (B) UCSF criteria. The curves represent the overall survival prediction according to HepatoPredict Class I+II (blue lines) versus Milan or UCSF Criteria (gray lines).