Gene expression analysis of biopsy samples reveals critical limitations of transcriptome‐based molecular classifications of hepatocellular carcinoma. (5th February 2016)

• Record Type: Journal Article
• Authors: Makowska, Zuzanna; Boldanova, Tujana; Adametz, David; Quagliata, Luca; Vogt, Julia E.; Dill, Michael T.; Matter, Mathias S.; Roth, Volker; Terracciano, Luigi; Heim, Markus H.
• Is Part Of: Journal of pathology. Volume 2:Number 2(2016)
• Page Start: 80
• Page End: 92
• Publication Date: 2016-02-05
• Subjects: hepatocellular carcinoma -- molecular classification -- transcriptome -- gene expression; Pathology -- Periodicals; Diagnosis, Laboratory -- Periodicals; 616.07
• DOI: 10.1002/cjp2.37
• Languages: English
• ISSNs: 2056-4538

Abstract

Abstract: 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 expression data from paired HCC and non‐cancerous liver tissue biopsies from 60 patients as well as five normal liver samples. Unbiased consensus clustering of HCC biopsy profiles identified 3 robust classes. Class membership correlated with survival, tumour size and with Edmondson and Barcelona Clinical Liver Cancer (BCLC) stage. When focusing only on the gene expression of the HCC biopsies, we could validate previously reported classifications of HCC based on expression patterns of signature genes. However, the subclass‐specific gene expression patterns were no longer preserved when the fold‐change relative to the normal tissue was used. The majority of genes believed to be subclass‐specific turned out to be cancer‐related genes differentially regulated in all HCC patients, 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. In conclusion, we have found 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.