Despite advances in therapeutics, the progression of melanoma to metastasis still confers a poor outcome to patients. Nevertheless, there is a scarcity of biological models to understand cellular and molecular changes taking place along disease progression. Here, we characterized the transcriptome profiles of a multi-stage murine model of melanoma progression comprising a nontumorigenic melanocyte lineage (melan-a), premalignant melanocytes (4C), nonmetastatic (4C11-) and metastasis-prone (4C11+) melanoma cells. Clustering analyses have grouped the 4 cell lines according to their differentiated (melan-a and 4C11+) or undifferentiated/“mesenchymal-like” (4C and 4C11-) morphologies, suggesting dynamic gene expression patterns associated with the transition between these phenotypes. The cell plasticity observed in the murine melanoma progression model was corroborated by molecular markers described during stepwise human melanoma differentiation, as the differentiated cell lines in our model exhibit upregulation of transitory and melanocytic markers, whereas “mesenchymal-like” cells show increased expression of undifferentiated and neural crest-like markers. Sets of differentially expressed genes (DEGs) were detected at each transition step of tumor progression, and transcriptional signatures related to malignancy, metastasis and epithelial-to-mesenchymal transition were identified. Finally, DEGs were mapped to their human orthologs and evaluated in uni- and multivariate survival analyses using gene expression and clinical data of 703 drug-naïve primary melanoma patients, revealing several independent candidate prognostic markers. Altogether, these results provide novel insights into the molecular mechanisms underlying the phenotypic switch taking place during melanoma progression, reveal potential drug targets and prognostic biomarkers, and corroborate the translational relevance of this unique sequential model of melanoma progression.

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Pancreatic ductal adenocarcinoma (PDAC) is one of the most deadly human malignancies. The only curative treatment available is the surgical removal of the tumor in early stages of the disease. Current methods for early detection and treatment are poor, justifying more studies in this field. We aim to generate a high-resolution catalog of the PDAC transcriptome, to reveal transcriptional alterations associated with the malignant phenotype of PDAC. To that end 28 patient matched samples of PDAC and nontumor adjacent pancreatic tissue were processed for the production of rRNA subtracted cDNAs libraries and subsequently sequenced in the Illumina HiSeq platform producing 17 million 100 nt paired-end reads per sample on average. We are implementing an informatics pipeline to detect and evaluate the differential expression in PDAC of well-annotated genes, including long noncoding RNAs (lncRNAs). In addition, we will search for novel lncRNAs and alternative splicing isoforms of protein-coding genes expressed in pancreatic tissues. Following the initial quality control (FastQC) and trimming of low quality regions (Trimmomatic), reads from each sample were aligned to the human genome (TopHat2) and a count table with the number of reads per gene for each sample was created (HTSeq). For evidence of changes across experimental conditions we use DESeq2, identifying 310 genes up-regulated and 354 down-regulated (p-adjusted<0.001 and lfc<|3.3|), including 156 lncRNAs. The differentially expressed genes detected are involved in pathways related to cancer, regulation of cell cycle process, membrane and secreted proteins. De novo transcript assembly (Trinity) revealed a number of yet unannotated transcripts and isoforms expressed in pancreatic tissues. Altogether, the transcript catalog generated herein provides a valuable resource for the identification of novel putative candidate biomarkers to improve the early detection or evaluation of therapeutic response in PDAC. Work supported by FAPESP, CNPq and CAPES. Citation Format: Omar J. Sosa, Vinícius F. Paixão, João C. Setubal, Eduardo M. Reis. Bioinformatic analysis of RNA-Seq data to search for novel diagnostic/prognostic biomarkers of pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 105.

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