Molecular Classification of Cancer : Unsupervised Self-Organizing Map Analysis of Gene Expression Microarray Data 1

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 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. Introduction The challenge of human cancer classifications will require methodologies capable of accurately assessing over 100 tumor types and an even larger number of tumor subtypes (1). Recent efforts to develop algorithmic methods for multiclass tumor classifications based on fingerprints of tumor gene expression profiles offer considerable hope toward precise, objective, and systematic cancer diagnosis (2–8) More importantly, these successes extend beyond disease classifications by providing a basis for molecular targeted therapies (9, 10). These early stages of cancer classification methodologies have raised many questions, which fall primarily into two broad areas: one involving analytical issues related to the computational and statistical strategies; and the other related to the construction of comprehensive patient-based gene expression databases (11, 12). This paper will focus on the analytical issues of multiclass tumor classifications based on the publicly available oligonucleotide microarray samples available at the Whitehead site. Our analysis will develop an unsupervised SOMbased classification scheme using 190 patient tumor samples spanning 14 common tumor types and 90 normal tissue expression profiles. Our procedure is based on a fuzzy classification scheme that attempts to assign rankings of each tissue sample to all 14 tumor classes. Fuzzy is used here to indicate cases where an exact set of rules (genes) may not always yield clear and unique separations (tumor classes) among test samples (tissues). Our results will be contrasted with previously published, highly accurate, supervised classification schemes (2). These earlier supervised schemes have used binary classification strategies trained a priori according to tumor class membership as well as partitioning algorithms organized by class hierarchy (11). Our intent here is to offer an alternative methodology directed at the common goal of multiclass tumor assignments. The approach is general and can easily be implemented on any gene expression dataset across a large number of tumor classes. Noteworthy in our approach is the development of fuzzy classification tumor assignments, which can be demonstrated to identify unclassifiable tumor samples as well as characterize other samples possessing gene profiles characteristic of more than one tumor class. As our results will demonstrate, our classification accuracies match those obtained by others, suggesting that little can be gained from this alternative approach. Implicit in our procedure, however, is the absence of gene selection using a priori classification Received 9/30/02; revised 12/20/02; accepted 1/15/03. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. 1 This project has been funded in whole or in part with federal funds from the National Cancer Institute, NIH, under Contract No. NO1-CO-12400. 2 To whom requests for reprints should be addressed, at National Cancer Institute-Frederick, Science Applications International Corporation-Frederick, Developmental Therapeutics Program, Screening Technologies Branch, Laboratory of Computational Technologies, Building 1052, Room 238, Frederick, MD 21702. 3 www-genome.wi. mit.edu/MPR. 4 The abbreviations used are: SOM, self-organizing map; CNS, central nervous system; S2N, signal to noise; PCA, principal component analysis. 317 Vol. 2, 317–332, March 2003 Molecular Cancer Therapeutics