A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients

Abstract

Metastasis-related recurrence often occurs in hepatocellular carcinoma (HCC) patients who receive curative therapies. At present, it is challenging to identify patients with high risk of recurrence, which would warrant additional therapies. In this study, we sought to analyze a recently developed metastasis-related gene signature for its utility in predicting HCC survival, using 2 independent cohorts consisting of a total of 386 patients who received radical resection. Cohort 1 contained 247 predominantly HBV-positive cases analyzed with an Affymetrix platform, whereas cohort 2 contained 139 cases with mixed etiology analyzed with the NCI Oligo Set microarray platform. We employed a survival risk prediction algorithm with training, test, and independent cross-validation strategies and found that the gene signature is predictive of overall and disease-free survival. Importantly, risk was significantly predicted independently of clinical characteristics and microarray platform. In addition, survival prediction was successful in patients with early disease, such as small (<5 cm in diameter) and solitary tumors, and the signature predicted particularly well for early recurrence risk (<2 years), especially when combined with serum alpha fetoprotein or tumor staging. In conclusion, we have shown in 2 independent cohorts with mixed etiologies and ethnicity that the metastasis gene signature is a useful tool to predict HCC outcome, suggesting the general utility of this classifier. We recommend the use of this classifier as a molecular diagnostic test to assess the risk that an HCC patient will develop tumor relapse within 2 years after surgical resection, particularly for those with early-stage tumors and solitary presentation.

Figure 1

Survival risk prediction analysis and application of the metastasis gene signature. (A) Schematic overview of the study design. (B) Kaplan-Meier survival curves showing the overall survival (top panel; N = 242) and the disease-free survival (bottom panel; N = 242) of the predicted high and low risk groups in the LCI cohort. (C) Kaplan-Meier survival curves showing the overall survival (top panel; N = 113) and the disease-free survival (bottom panel; N = 64) of the predicted high and low risk groups in the LEC cohort. Displayed are the Cox-Mantel log-rank, the permutation p-values and the number of patients at risk for each Kaplan-Meier survival curve.

Figure 2

Analysis of the performance of the survival risk prediction dependent on HCC tumor recurrence over time after surgery. (A) Cumulative HCC recurrence rate over time. (B) Smoothed recurrence rate per month over time. (C) Forest plots showing Hazard Ratios for high risk patients in the indicated clinical groups of patients. Hazard Ratios are shown for the overall survival at 5 years, (D) the overall survival at 2 years, (E) the disease-free survival at 5 years and (F) the disease-free survival at 2 years of follow-up of the high risk subgroup as compared with the low risk group. Hazard ratios above 1.0 indicate significantly worse outcome. ND, not determined.

Figure 3

Unbiased cross-validation of the survival risk prediction and analysis of the sensitivity and specificity by Receiver Operating Characteristic (ROC) curves. (A) Six class prediction algorithms, i.e., Support Vector Machines (SVM), Nearest Centroid (NC), 3-Nearest Neighbor (3-NN), 1-Nearest Neighbor (1-NN), Linear Discriminant Analysis (LDA) or Compound Covariate Predictor (CCP) were used to predict good and poor survival HCC groups in the independent validation data set. Forest plots show Hazard Ratios for high risk patients in clinical groups of patients. Hazard Ratios are shown for the overall survival for the LCI cohort at 5 years using the LEC cohort as a training/test set and predicting outcome in the LCI cohort. (B) Hazard Ratios of the LEC cohort are shown using LCI as the training/test set and prediction of the LEC cohort are depicted. (C) ROC curve of the LCI cohort and (D) ROC curve of the LEC cohort applying the compound covariate predictor. AUC; area under the curve. SVM, Support Vector Machines, NC, Nearest Centroid, 3-NN, 3-Nearest Neighbor, 1-NN, 1-Nearest Neighbor, LDA, Linear Discriminant Analysis. CCP, Compound Covariate Predictor.

Figure 4

Combination of survival risk prediction applying the Compound Covariate Predictor (CCP) and AFP (300 ng/mL cutoff) to stratify patient subgroups. (A) The Kaplan-Meier curves show overall survival of the LCI cohort (N = 238) and (B) LEC cohort (N = 104) sub-grouped by survival risk prediction and AFP. Disconc.: cases with discordant risk assessments, i.e., high risk according to the metastasis risk classification and low risk prediction by AFP, i.e. AFP less than 300 ng/mL.

Figure 5. A

Patient stratification using survival risk prediction and BCLC staging. (A) Kaplan-Meier curves are showing overall survival of the LCI cohort (N = 225) by sub-grouping according to CCP class prediction of good or poor prognosis and BCLC stage 0-A or B–C. (B) Kaplan-Meier curves of patients with BCLC staging A (N = 153) stratified by CCP survival risk prediction. Disconc.: cases with discordant risk assessments, i.e., high risk according to the metastasis risk classification and early stage prediction by BCLC.