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. 2016 Apr;9(2):139-146.
doi: 10.1016/j.tranon.2016.02.003.

A Global Risk Score (GRS) to Simultaneously Predict Early and Late Tumor Recurrence Risk after Resection of Hepatocellular Carcinoma

Jeroen Dekervel  1 , Dusan Popovic  2 , Hannah van Malenstein  1 , Petra Windmolders  1 , Line Heylen  3 , Louis Libbrecht  4 , Ashenafi Bulle  1 , Bart De Moor  2 , Eric Van Cutsem  5 , Frederik Nevens  1 , Chris Verslype  6 , Jos van Pelt  7
Affiliations

A Global Risk Score (GRS) to Simultaneously Predict Early and Late Tumor Recurrence Risk after Resection of Hepatocellular Carcinoma

Jeroen Dekervel et al. Transl Oncol. 2016 Apr.

Abstract

Objectives: Recurrence of hepatocellular carcinoma can arise from the primary tumor ("early recurrence") or de novo from tumor formation in a cirrhotic environment ("late recurrence"). We aimed to develop one simple gene expression score applicable in both the tumor and the surrounding liver that can predict the recurrence risk.

Methods: We determined differentially expressed genes in a cell model of cancer aggressiveness. These genes were first validated in three large published data sets of hepatocellular carcinoma from which we developed a seven-gene risk score.

Results: The gene score was applied on two independent large patient cohorts. In the first cohort, with only tumor data available, it could predict the recurrence risk at 3 years after resection (68 ± 10% vs 35 ± 7%, P = .03). In the second cohort, when applied on the tumor, this gene score predicted early recurrence (62 ± 5% vs 37 ± 4%, P < .001), and when applied on the surrounding liver tissue, the same genes also correlated with late recurrence. Four patient classes with each different time patterns and rates of recurrence could be identified based on combining tumor and liver scores. In a multivariate Cox regression analysis, our gene score remained significantly associated with recurrence, independent from other important cofactors such as disease stage (P = .007).

Conclusions: We developed a Global Risk Score that is able to simultaneously predict the risk of early recurrence when applied on the tumor itself, as well as the risk of late recurrence when applied on the surrounding liver tissue.

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Figures

Figure 1
Figure 1
Training of the GRS. Performance was tested using an ROC curve in the Zhang (tumor versus nontumoral tissue), Kudo (multicentric occurrence versus no multicentric occurrence), and Chiang (BCLC C versus 0-B) data sets (Table 1).
Figure 2
Figure 2
Recurrence rates and overall survival in the LEC cohort . Low (solid line) versus high (dashed line) GRS patients were assessed in tumor tissue.
Figure 3
Figure 3
Recurrence rates and overall survival in the LCI cohort . Low (solid line) versus high (dashed line) GRS patients were assessed in tumor (first row) and liver (second row) tissue. Recurrence and death before 19 months were censored in the second and fourth column, respectively.
Figure 4
Figure 4
Combining tumor and liver gene expression data. (A) Recurrence rates and overall survival in the LCI cohort in the four GRS classes (GRS I = low GRS in tumor and liver; GRS II = low GRS in tumor, high in liver; GRS III = high GRS in tumor, low in liver; GRS IV = high GRS in tumor and liver). (B) Mean overall survival time for different GRS classes. (C) Mean disease-free survival time for different GRS classes.
See this image and copyright information in PMC

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