Molecular classification of cancer: unsupervised self-organizing map analysis of gene expression microarray data

An unsupervised self-organizing map-based clustering strategy has been developed to classify tissue samples from an oligonucleotide microarray patient database. Our method is based on the likelihood that a test data vector may have a gene expression fingerprint that is shared by more than one tumor...

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Bibliographic Details
Published in:Molecular cancer therapeutics 2003-03, Vol.2 (3), p.317-332
Main Authors: Covell, David G, Wallqvist, Anders, Rabow, Alfred A, Thanki, Narmada
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biomarkers, Tumor
Computational Biology
Databases, Factual
DNA, Neoplasm - analysis
Gene Expression Profiling
Humans
Neoplasms - classification
Neoplasms - genetics
Neoplasms - pathology
Oligonucleotide Array Sequence Analysis - methods
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Summary:An unsupervised self-organizing map-based clustering strategy has been developed to classify tissue samples from an oligonucleotide microarray patient database. Our method is based on the likelihood that a test data vector may have a gene expression fingerprint that is shared by more than one tumor class and as such can identify datasets that cannot be unequivocally assigned to a single tumor class. Our self-organizing map analysis completely separated the tumor from the normal expression datasets. Within the 14 different tumor types, classification accuracies on the order of approximately 80% correct were achieved. Nearly perfect classifications were found for leukemia, central nervous system, melanoma, uterine, and lymphoma tumor types, with very poor classifications found for colorectal, ovarian, breast, and lung tumors. Classification results were further analyzed to identify sets of differentially expressed genes between tumor and normal gene expressions and among each tumor class. Within the total pool of 1139 genes most differentially expressed in this dataset, subsets were found that could be vetted according to previously published literature sources to be specific tumor markers. Attempts to classify gene expression datasets from other sources found a wide range of classification accuracies. Discussions about the utility of this method and the quality of data needed for accurate tumor classifications are provided.
ISSN:1535-7163