Recursive partitioning for tumor classification with gene expression microarray data

Abstract

Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for purposes of comparison, we introduce a methodology based on classification trees and demonstrate that it is significantly more accurate for discriminating among distinct colon cancer tissues than other statistical approaches used heretofore. In addition, competing classification trees are displayed, which suggest that different genes may coregulate colon cancers.

Figure 1

Classification trees for tissue types by using expression data from three genes (M26383, R15447, M28214). Circles represent internal nodes that are subsequently divided into daughter nodes. The boxes are terminal nodes that do not have further partition and determine the tissue class membership; the red ones contain a total of 40 cancer tissues and 1 normal tissue, and the green ones contain 21 normal tissues. Beneath each internal node is the gene whose expression level is used to split the node, and the cutoff is displayed on the arrow next to the right. The four companion tables provide the information of the predictive precision of the tree based on a cross-validation scheme; see text for details. CT, number of cancer tissues; NT, number of normal tissues.

Figure 2

A scatter plot of expression data from M26383 and R15447. The dots are colored in green and red for normal and cancer tissues, respectively. The dotted line marks the cutoff value for node 1 in Fig. 1, and the two regions are labeled with their corresponding nodes in the same figure.

Figure 3

A scatter plot of expression data from R15447 and M28214 for a subset of tissues (node 3 in Fig. 1). The dots are colored green and red for normal and cancer tissues, respectively. The dotted lines mark the cutoff values for nodes 3 and 4 in Fig. 1, and the three regions are labeled with their corresponding nodes in the same figure.

Figure 4

Three-dimensional illustration of gene expressions from M26383, R15447, and M28214, along with tissue types. The 40 points from cancer tissues are labeled in red and the 22 points from normal tissues in green. Because cancer tissues end up in two terminal nodes in Fig. 1 and so are normal tissues, two levels of intensities for each of the red and green colors are highlighted to indicate different terminal node assignments of the same type of tissues.

Figure 5

Correlation curves between the three selected gene expressions in Fig. 1 and the remaining expression data. Genes are sorted according to the absolute correlation levels with one of the three selected genes and, obviously, the orders are different among the three selected genes.

Figure 6

Classification trees for tissue types by using expression data from three genes (R87126, T62947, X15183). Circles represent internal nodes that are subsequently divided into daughter nodes. The boxes are terminal nodes that do not have further partition and determine the tissue class membership; the red ones contain a total of 40 cancer tissues, and the green ones contain 22 normal tissues. Beneath each internal node is the gene whose expression level is used to split the node, and the cutoff is displayed on the arrow next to the right. The four companion tables provide the information of the predictive precision of the tree based on a cross-validation scheme; see text for details. CT, number of cancer tissues; NT, number of normal tissues.

Figure 7

Three-dimensional illustration of the gene expressions from X15183, R87126, and T62947, along with tissue types.