Poster Presentation 25th Lorne Cancer Conference 2013

A bioinformatic pipeline for the analysis of miRNAs in the PMC42 and MDA-MB-468 model systems of human breast cancer. (#309)

Gayle K Philip 1 , Tony Blick 2 , Raj Gaire 3 , Eliza Soo 2 , Bryce van Denderen 2 4 , Izhak Haviv 5 , Erik W Thompson 2 6
  1. Life Science Computation Centre, Carlton, VIC, Australia
  2. St Vincent's Insitute, Melbourne, Australia
  3. Baker IDI Heart and Diabetes Institute, Melbourne, Australia
  4. University of Melbourne Department of Medicine, St. Vincent’s Hospital, Melbourne, VIC, Australia
  5. Faculty of Medicine, Bar Ilan University, Safed, Israel
  6. University of Melbourne Department of Surgery, St. Vincent’s Hospital, Melbourne, Australia

Epithelial-mesenchymal (EMT) and mesenchymal-epithelial (MET) transitions are important during normal development, in pathologic states such as fibrosis and in carcinoma progression to metastases. It appears that, during the process of metastasis, mesenchymal features support local invasion and therapy resistance while epithelialisation supports colonisation at distant sites. Hybrid epithelial-mesenchymal states (with cells expressing both E-cadherin and vimentin) are seen in carcinoma subpopulations, suggestive of a fluid set of transitions that we have called epithelial-mesenchymal plasticity (EMP).

We have used next-generation sequencing (miRNA-seq) to sequence microRNAs (miRNAs), a class of non-coding RNAs that play a key role in the regulation of gene expression, in the PMC42 and MDA-MB-468 human breast cancer cell lines. These systems provide a model of EMP and respond differentially to external signals such as EGF and hypoxia. We present the pipeline developed to process and analyse these sequences. Central to the miRNA-seq analysis pipeline is the identification of novel and known miRNAs in deep sequencing data, as well as the expression profiling across samples to determine the differentially expressed miRNAs. Additionally, in order to understand the genes, or networks of genes, whose expression they regulate, we have generated a comprehensive list of associated mRNA gene targets using both manually curated, experimentally supported microRNA targets databases, as well as targets predicted by a variety of target prediction algorithms.

This work was supported by the NBCF National Collaborative Research Program: EMPathyTargeting Breast Cancer Recurrence through Epithelial Mesenchymal Plasticity.