title: [Comparative genomic classification of human hepatocellular carcinoma].
abstract: Global transcriptome analysis has been successfully applied to characterize various human tumors, including hepatocellular carcinomas. This novel technology can facilitate early diagnosis, as well as prognostic and therapeutic diversification of cancer patients. To enhance access to the genomic information buried in archived pathology samples, we assessed RT-PCR amplification rates in paraffin-embedded tissues preserved in three different fixatives. Reliable amplification could be achieved from all paraffin-embedded specimens, when the amplicon size did not exceed 225 bp. A longer amplicon size resulted in rapid decrease of yield and reproducibility. In addition, formalin provided superior morphology and better reactivity with claudin-4 and -7 immunohistochemistry. Amplification of the initial sample is often required before transcriptome analysis of clinical specimens could be performed. We introduced a random nonamer primed T3 polymerase reaction into the conventional linear RNA amplification protocol. The modified T3T7 method generated a sense strand product ideal for synthesizing indirectly labeled cDNA templates. Microarray analysis of amplified frozen and laser-microdissected Myc and Myc/TGFalpha mouse liver tumors confirmed good reproducibility (r=0.9) of the reaction and conservation of original transcriptional patterns (r=0.78). Finally, we tested the utility of expression profiling for the classification of human HCC samples. By comparing expression data from HGF-treated c-Met conditional knock-out and control primary mouse hepatocytes, we identified 690 HGF/c-Met target genes. Functional analysis of the significant gene set implicated c-Met as key regulator of hepatocyte motility and oxidative homeostasis. Cross comparison of the c-Met-induced transcription signature with human HCC expression profiles revealed a group of tumors (27%) with potentially activated c-Met signaling (MET+). These tumors were characterized by higher vascular invasion rate, increased microvessel density, and shortened survival. A prediction model based on 111 cross-species conserved c-Met signature genes was able to diversify HCC patients into good and bad prognostic groups with 83-95% accuracy. Our results therefore demonstrate that careful experimental design and state-of-the-art laboratory methods could open the way for global expression profiling of archived and limited availability pathologic samples. Comparative functional genomics based analysis of the cancer transcriptome could lead to novel molecular classification systems which are essential for the introduction of individualized cancer therapeutics.
authors.affiliation: Semmelweis Egyetem Klinikai Orvostudományok Doktori Iskola Budapest Semmelweis Egyetem II. sz. Patológiai Intézet 1091 Budapest Ulloi út 93, Hungary.
authors.forename: Pál
authors.initials: P
authors.lastname: Kaposi-Novák