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

Abstract

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 (BCLC 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 non-tumor, 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 non-tumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature “G3-proliferation” (hazard ratio [HR]=1.75, P=0.003) and an adjacent “poor-survival” signature (HR=1.74, P=0.004) were independent predictors of HCC recurrence, along with satellites (HR=1.66, P=0.04). 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.

Integrated transcriptomic and proteomic analyses reveal ɑ‐lipoic acid‐regulated cell proliferation via Grb2‐mediated signalling in hepatic cancer cells

“…Gene expression data for tumours and adjacent non‐tumour cirrhotic formalin‐fixed, paraffin‐embedded tissues were conducted using the 6k Transcriptionally Informative Gene Panel for DASL (Illumina, GEO‐GPL5474) (Table S1) 14, 15.…”

HLA and Killer Immunoglobulin-like Receptor Genes as Outcome Predictors of Hepatitis C Virus–Related Hepatocellular Carcinoma

“…The direct clinical consequences of the underlying liver disease have also a major impact on OS after treatment. Because the biologic characteristics of the tumor and the host immune response are both involved in hepatocellular carcinoma progression (25)(26)(27), it is likely that the immunogenetic background determining the NKcell response may play a role on the risk of relapse and ultimately on the survival of patients with hepatocellular carcinoma. Figure 2.…”

Immunohistochemistry panel segregates molecular types of hepatocellular carcinoma in Brazilian autopsy cases

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