Transcriptomics,Genomics

Dataset Information

112

Gene expression profiling in paired human hepatocellular carcinoma and liver parenchyma biopsies and normal liver biopsies.


ABSTRACT: Hepatocellular carcinoma (HCC) is a heterogeneous disease, and despite considerable research efforts, no molecular classification of HCC has been introduced in clinical practice. The existing molecular classification systems were established using resected tumors, which introduces a selection bias towards patients without liver cirrhosis and with early stage HCCs. So far, these classification systems have not been validated in liver biopsy specimens from tumors diagnosed at intermediate and late stages. We generated and analyzed expression profiles from 60 HCC biopsies from an unselected patient population with all tumor stages. Unbiased clustering identified 3 HCC classes. Class membership correlated with survival, tumor size, and with Edmondson and BCLC stage. Most biopsy specimens could be assigned to the classes of published classification systems, demonstrating that gene expression profiles obtained from patients with early stage disease are preserved in all stages of HCC. When a reference set of healthy liver samples was integrated in the analysis, we observed that the differentially regulated genes up- or down-regulated in a given class relative to other classes were actually dysregulated in the same direction in all HCCs, with quantitative rather than qualitative differences between the molecular subclasses. With the exception of a subset of samples with a definitive β-catenin gene signature, biological pathway analysis could not identify class specific pathways reflecting the activation of distinct oncogenic programs. Our results suggest that gene expression profiling of HCC biopsies has limited potential to direct therapies that target specific driver pathways, but can identify subgroups of patients with different prognosis. Overall design: 60 biopsy pairs from hepatocellular carcinoma patients (1 biopsy of the tumor and 1 biopsy of the non-tumor liver from each patient), 5 normal liver biopsies

INSTRUMENT(S): [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version]

SUBMITTER: Zuzanna Makowska 

PROVIDER: GSE64041 | GEO | 2016-05-01

SECONDARY ACCESSION(S): PRJNA269951

REPOSITORIES: GEO

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Publications

Gene expression analysis of biopsy samples reveals critical limitations of transcriptome-based molecular classifications of hepatocellular carcinoma.

Makowska Zuzanna Z   Boldanova Tujana T   Adametz David D   Quagliata Luca L   Vogt Julia E JE   Dill Michael T MT   Matter Mathias S MS   Roth Volker V   Terracciano Luigi L   Heim Markus H MH  

The Journal of Pathology: Clinical Research 20160205 2


Molecular classification of hepatocellular carcinomas (HCC) could guide patient stratification for personalized therapies targeting subclass-specific cancer 'driver pathways'. Currently, there are several transcriptome-based molecular classifications of HCC with different subclass numbers, ranging from two to six. They were established using resected tumours that introduce a selection bias towards patients without liver cirrhosis and with early stage HCCs. We generated and analyzed gene expressi  ...[more]

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