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Analysis of Normal-Tumour Tissue Interaction in Tumours: Prediction of Prostate Cancer Features from the Molecular Profile of Adjacent Normal Cells

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  • Victor Trevino
  • Mahlet G Tadesse
  • Marina Vannucci
  • Fatima Al-Shahrour
  • Philipp Antczak
  • Sarah Durant
  • Andreas Bikfalvi
  • Joaquin Dopazo
  • Moray J Campbell
  • Francesco Falciani

Abstract

Statistical modelling, in combination with genome-wide expression profiling techniques, has demonstrated that the molecular state of the tumour is sufficient to infer its pathological state. These studies have been extremely important in diagnostics and have contributed to improving our understanding of tumour biology. However, their importance in in-depth understanding of cancer patho-physiology may be limited since they do not explicitly take into consideration the fundamental role of the tissue microenvironment in specifying tumour physiology. Because of the importance of normal cells in shaping the tissue microenvironment we formulate the hypothesis that molecular components of the profile of normal epithelial cells adjacent the tumour are predictive of tumour physiology. We addressed this hypothesis by developing statistical models that link gene expression profiles representing the molecular state of adjacent normal epithelial cells to tumour features in prostate cancer. Furthermore, network analysis showed that predictive genes are linked to the activity of important secreted factors, which have the potential to influence tumor biology, such as IL1, IGF1, PDGF BB, AGT, and TGFβ.

Suggested Citation

  • Victor Trevino & Mahlet G Tadesse & Marina Vannucci & Fatima Al-Shahrour & Philipp Antczak & Sarah Durant & Andreas Bikfalvi & Joaquin Dopazo & Moray J Campbell & Francesco Falciani, 2011. "Analysis of Normal-Tumour Tissue Interaction in Tumours: Prediction of Prostate Cancer Features from the Molecular Profile of Adjacent Normal Cells," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-13, March.
    • Handle: RePEc:plo:pone00:0016492
    DOI: 10.1371/journal.pone.0016492
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    1. Janine T. Erler & Kevin L. Bennewith & Monica Nicolau & Nadja Dornhöfer & Christina Kong & Quynh-Thu Le & Jen-Tsan Ashley Chi & Stefanie S. Jeffrey & Amato J. Giaccia, 2006. "Lysyl oxidase is essential for hypoxia-induced metastasis," Nature, Nature, vol. 440(7088), pages 1222-1226, April.
    2. Naijun Sha & Marina Vannucci & Mahlet G. Tadesse & Philip J. Brown & Ilaria Dragoni & Nick Davies & Tracy C. Roberts & Andrea Contestabile & Mike Salmon & Chris Buckley & Francesco Falciani, 2004. "Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage," Biometrics, The International Biometric Society, vol. 60(3), pages 812-819, September.
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