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Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction

Gerald Quon19, Syed Haider23, Amit G Deshwar4, Ang Cui5, Paul C Boutros26* and Quaid Morris1478*

Author Affiliations

1 Department of Computer Science, University of Toronto, 10 King's College Road, Room 3302, Toronto, ON, Canada, M5S 3G4

2 Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, 101 College Street, Suite 800, Toronto, ON, Canada, M5G 0A3

3 Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, United Kingdom, CB3 0FD

4 Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Room SFB540, Toronto, ON, Canada, M5S 3G4

5 Division of Engineering Science, University of Toronto, 40 St. George Street, Suite 2110, Toronto, ON, Canada, M5S 2E4

6 Department of Medical Biophysics, University of Toronto, 610 University Avenue, Room 7-411, Toronto, ON, Canada, M5G 2M9

7 Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Room 4396, Toronto, ON, Canada, M5S 1A8

8 The Donnelly Centre, 160 College Street, Room 230, Toronto, ON, Canada, M5S 3E1

9 Current address: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA

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Genome Medicine 2013, 5:29  doi:10.1186/gm433

Published: 28 March 2013

Abstract

Tumor heterogeneity is a limiting factor in cancer treatment and in the discovery of biomarkers to personalize it. We describe a computational purification tool, ISOpure, which directly addresses the effects of variable contamination by normal tissue in clinical tumor specimens. ISOpure uses a set of tumor expression profiles and a panel of healthy tissue expression profiles to generate a purified cancer profile for each tumor sample, and an estimate of the proportion of RNA originating from cancerous cells. Applying ISOpure before identifying gene signatures leads to significant improvements in the prediction of prognosis and other clinical variables in lung and prostate cancer.