Quantitative proteomics and phosphoproteomics on serial tumor biopsies from a sorafenib-treated HCC patient.

Compensatory signaling pathways in tumors confer resistance to targeted therapy, but the pathways and their mechanisms of activation remain largely unknown. We describe a procedure for quantitative proteomics and phosphoproteomics on snap-frozen biopsies of hepatocellular carcinoma (HCC) and matched nontumor liver tissue. We applied this procedure to monitor signaling pathways in serial biopsies taken from an HCC patient before and during treatment with the multikinase inhibitor sorafenib. At diagnosis, the patient had an advanced HCC. At the time of the second biopsy, abdominal imaging revealed progressive disease despite sorafenib treatment. Sorafenib was confirmed to inhibit MAPK signaling in the tumor, as measured by reduced ribosomal protein S6 kinase phosphorylation. Hierarchical clustering and enrichment analysis revealed pathways broadly implicated in tumor progression and resistance, such as epithelial-to-mesenchymal transition and cell adhesion pathways. Thus, we describe a protocol for quantitative analysis of oncogenic pathways in HCC biopsies and obtained first insights into the effect of sorafenib in vivo. This protocol will allow elucidation of mechanisms of resistance and enable precision medicine.

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

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 ß-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.