Tumor classification ranking from microarray data

Abstract

Background: Gene expression profiles based on microarray data are recognized as potential diagnostic indices of cancer. Molecular tumor classifications resulted from these data and learning algorithms have advanced our understanding of genetic changes associated with cancer etiology and development. However, classifications are not always perfect and in such cases the classification rankings (likelihoods of correct class predictions) can be useful for directing further research (e.g., by deriving inferences about predictive indicators or prioritizing future experiments). Classification ranking is a challenging problem, particularly for microarray data, where there is a huge number of possible regulated genes with no known rating function. This study investigates the possibility of making tumor classification more informative by using a method for classification ranking that requires no additional ranking analysis and maintains relatively good classification accuracy.

Results: Microarray data of 11 different types and subtypes of cancer were analyzed using MDR (Multi-Dimensional Ranker), a recently developed boosting-based ranking algorithm. The number of predictor genes in all of the resulting classification models was at most nine, a huge reduction from the more than 12 thousands genes in the majority of the expression samples. Compared to several other learning algorithms, MDR gives the greatest AUC (area under the ROC curve) for the classifications of prostate cancer, acute lymphoblastic leukemia (ALL) and four ALL subtypes: BCR-ABL, E2A-PBX1, MALL and TALL. SVM (Support Vector Machine) gives the highest AUC for the classifications of lung, lymphoma, and breast cancers, and two ALL subtypes: Hyperdiploid > 50 and TEL-AML1. MDR gives highly competitive results, producing the highest average AUC, 91.01%, and an average overall accuracy of 90.01% for cancer expression analysis.

Conclusion: Using the classification rankings from MDR is a simple technique for obtaining effective and informative tumor classifications from cancer gene expression data. Further interpretation of the results obtained from MDR is required. MDR can also be used directly as a simple feature selection mechanism to identify genes relevant to tumor classification. MDR may be applicable to many other classification problems for microarray data.

Figure 1

ROC curves on prostate cancer expressions

Figure 2

Basic steps of MDR

Figure 3

Example of a model produced by MDR

Figure 4

Performance metrics in binary classification

Figure 5

ROC curves on breast cancer classification of the same accuracy