Gene Signature Selection for Cancer Prediction Using an Integrated Approach of Genetic Algorithm and Support Vector Machine

Abstract

Classification of tumor types based on genomic information is essential for improving future cancer diagnosis and drug development. Since DNA microarray studies produce a large amount of data, effective analytical methods have to be developed to sort out whether specific cancer samples have distinctive features of gene expression over normal samples or other types of cancer samples. In this paper, an integrated approach of support vector machine (SVM) and genetic algorithm (GA) is proposed for this purpose. The proposed approach can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. As an illustration, the proposed approach is applied in searching the combinational gene signatures for predicting histologic response to chemotherapy of osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that the proposed approach outperforms other existing methods in terms of classification accuracy. Further validation using an independent dataset shows misclassification of only one of fourteen patient samples suggesting that the selected gene signatures can reflect the chemoresistance in osteosarcoma.