Multi-omic analysis of Epithelial-Mesenchymal Transition in pancreatic cancer — ASN Events
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  3. Multi-omic analysis of Epithelial-Mesenchymal Transition in pancreatic cancer

Multi-omic analysis of Epithelial-Mesenchymal Transition in pancreatic cancer (#104)

Shivashankar H Nagaraj 1 , Magdalene Michael 2 , Nicola Waddell 3 , Alun Jones 4 , John V Pearson 3 , Alpha S Yap 4 , Erik (Rik) Thompson 1 , Sean M Grimmond 5
  1. Queensland University of Technology, Kelvin Grove, QLD, Australia
  2. Randall Division of Cell and Molecular Biophysics , King's College London , London, UK
  3. Cancer Genomics, QIMR Berghofer, Brisbane , QLD, Australia
  4. Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
  5. Wolfson Wohl Cancer Research Centre, University of Glasgow, Glasgow, Scotland

Epithelial to mesenchymal transition (EMT) is an evolutionarily conserved, complex biological process that plays a central role in development, tissue repair, formation of stem cells and cancer progression and metastasis. Accordingly, it has been hailed as an important but yet-to-be-realised opportunity in cancer therapy. EMT has been recently shown to promote formation of pancreatic tumours, for which there is ~5% survival rate (Rim et al, Cell 2012).Recognizing the underlying scale and complexity, we hypothesized that EMT can be better characterized using an integrated systems biology approach and undertook a multi-omic study in PANC-1 human pancreatic carcinoma cells. EMT was induced using transforming growth factor beta in a time dependent manner (0-72hrs). Multiple high-throughput technologies were used to profile the epigenome, transcriptome, proteome and phosphoproteome from PANC-1 cells in epithelial and mesenchymal states.

In parallel, we developed an informatics framework called as PGTools for the comprehensive analysis of multi-omic data. The software consists of a suite of programs for efficient analysis and management of high-throughput ‘omics’ datasets. Using our automated software, proteomic data procured from any mass-spectrometer can be directly compared with genomic and transcriptomic data generated by next-generation sequencing. PGTools provides the informatics framework to study complex biological processes, offers new directions for EMT research and with the potential for therapeutic interventions and improves our understanding of cell state, function, shape and polarity.

DNA methylation arrays (21,000), RNA-sequencing (21,612), SILAC-based mass spectrometry (4,602) and phospho-proteomics (3,818) in epithelial and mesenchymal states were simultaneously measured from the same cell population. Integrative systems analyses recapitulated known EMT signatures as well as identified candidate genes, pathways and regulatory networks underpinning EMT.

We have uncovered previously unknown molecules and molecular events associated with EMT, suggesting that EMT is far more complex than currently perceived.