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An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models

Xin Lu1*, Ke Zhang2, Charles Van Sant3, John Coon4 and Dimitri Semizarov1*

Author Affiliations

1 Global Pharmaceutical Research and Development, Abbott Laboratories, 100 Abbott Park Road, Building AP-10, Dep. R4CD, Abbott Park, IL 60064, USA

2 Department of Pathology, School of Medicine & Health Sciences, University of North Dakota, 501 N. Columbia Road, Grand Forks, ND 58202, USA

3 Astellas Pharma Global Development, INC. 8045 Lamon Ave, Skokie, IL 60077, USA

4 Department of Pathology, Rush University Medical Center, Chicago, IL 60612, USA

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BMC Medical Genomics 2010, 3:23  doi:10.1186/1755-8794-3-23

Published: 22 June 2010



Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.


To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.


We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.


We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.