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Bioinformatics Advance Access published online on June 19, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp378
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

ISOLATE: A computational strategy for identifying the primary origin of cancers using high throughput sequencing

Gerald Quon 1,2,* and Quaid Morris 1,2,3,4,*

1Department of Computer Science, 2Banting and Best Department of Medical Research, 3Department of Molecular Genetics, 4Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada

*To whom correspondence should be addressed. Mr. Gerald Quon, E-mail: gerald.quon{at}utoronto.ca, gerald.quon{at}gmail.com


  Abstract

Motivation: One of the most deadly cancer diagnoses is the carcinoma of unknown primary origin. Without knowledge of the site of origin, treatment regimens are limited in their specificity and result in high mortality rates. Though supervised classification methods have been developed to predict the site of origin based on gene expression data, they require large numbers of previously classified tumors for training, in part because they do not account for sample heterogeneity, which limits their application to well studied cancers.

Results: We present ISOLATE, a new statistical method that simultaneously predicts the primary site of origin of cancers and addresses sample heterogeneity, while taking advantage of new high throughput sequencing technology that promises to bring higher accuracy and reproducibility to gene expression profiling experiments. ISOLATE makes predictions de novo, without having seen any training expression profiles of cancers with identified origin. Compared to previous methods, ISOLATE is able to predict the primary site of origin, de-convolve and remove the effect of sample heterogeneity, and identify differentially expressed genes with higher accuracy, across both synthetic and clinical datasets. Methods such as ISOLATE are invaluable tools for clinicians faced with carcinomas of unknown primary origin.

Availability: ISOLATE is available for download at: http://morrislab.med.utoronto.ca/software

Contact: {gerald.quon, quaid.morris}@utoronto.ca

Associate Editor: Dr. Alex Bateman

Received on April 28, 2009; revised on June 9, 2009; accepted on June 15, 2009

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