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Comparative Study
, 140 (5), 1501-12.e2

Combining Clinical, Pathology, and Gene Expression Data to Predict Recurrence of Hepatocellular Carcinoma

Comparative Study

Combining Clinical, Pathology, and Gene Expression Data to Predict Recurrence of Hepatocellular Carcinoma

Augusto Villanueva et al. Gastroenterology.


Background & aims: In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (Barcelona-Clinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues.

Methods: We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence.

Results: Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified.

Conclusions: We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses.


Figure 1
Figure 1. Flow chart of the study
We collected genomic information from 287 HCC patients treated with surgical resection in 4 different institutions. All patients had tumors profiled using genome-wide platforms, whereas in 226 there was also genomic data available from the non-tumoral adjacent cirrhotic tissue. For prognosis studies, we focused on those patients with single nodule, early stage (BCLC 0/A) HCC (see methods for details).
Figure 2
Figure 2. Concordance of poor-outcome signatures in the whole set (n=287)
Figure includes only those signatures that confidently identified patients within their respective poor-outcome class (FDR<0.05). Left panel: Each column represents prediction for each patient. Red bars mean positive prediction for the signature, gray bars mean absence of prediction, and white bars that there was no genomic data available for that sample. Signatures are organized according to the type of tissue where they were generated and evaluated (tumor in the top and non-tumoral cirrhotic adjacent in the bottom). Right panel: Heatmap of Cramer’s V coefficients corresponding to signature pair wise comparisons. Signatures are clustered according to their degree of overlap (signatures generated from adjacent tissue are shown in green).
Figure 3
Figure 3. Kaplan-Meier estimates of early and overall recurrence according to G3 (tumor) and poor-survival (adjacent non-tumoral cirrhotic tissue) signature status
Top panels show results of patients with genomic data available from the tumor (n=244), whereas lower panels show results of those with genomic information from tumor and adjacent tissue (n=201). P values were obtained from the log-rank test. (+) denotes observations that were censored owing to loss of follow-up or the date the last contact.
Figure 4
Figure 4. Random survival forests for early (top panel) and overall (lower panel) recurrence
Left figures show the error rate according to the number of trees generated and right panel shows VIMP values for each variable evaluated. Adjacent tables show the average VIMP values for each variable after 100 runs.

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