A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma.

Journal: BMC genomics
PMID: 28166733

Abstract

BACKGROUND: We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules.

Authors

  • Gregory P Way
    Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA, USA.
  • Robert J Allaway
    Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Dartmouth College, HB 7650, Hanover, NH, 03755, USA.
  • Stephanie J Bouley
    Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Dartmouth College, HB 7650, Hanover, NH, 03755, USA.
  • Camilo E Fadul
    Department of Neurology, University of Virginia, Charlottesville, VA, USA.
  • Yolanda Sanchez
    Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Dartmouth College, HB 7650, Hanover, NH, 03755, USA. Yolanda.Sanchez@dartmouth.edu.
  • Casey S Greene
    Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, United States; Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, United States; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Perelman School of Medicine, University of Pennsylvania, United States. Electronic address: csgreene@upenn.edu.

Keywords

External Resources

Popular Topics
Recent Journals