Use of extreme patient samples for outcome prediction from gene expression data

Motivation: Patient outcome prediction using microarray technologies is an important application in bioinformatics. Based on patients ’ genotypic microarray data, predictions are made to estimate patients ’ survival time and their risk of tumor metastasis or recurrence. So, accurate prediction can potentially help to provide better treatment for patients. Results: We present a new computational method for pati-ent outcome prediction. In the training phase of this method, we make use of two types of extreme patient samples: short-term survivors who got an unfavorable outcome within a short period and long-term survivors who were maintaining a favo-rable outcome after a long follow-up time. These extreme training samples yield a clear platform for us to identify relevant genes whose expression is closely related to the out-come. The selected extreme samples and the relevant genes are then integrated by a support vector machine to build a pre-diction model, by which each validation sample is assigned a risk score that falls into one of special pre-defined risk groups. We apply this method to several public data sets. In most cases, patients in high and low risk groups stratified by our method have clearly distinguishable outcome status as seen in their Kaplan-Meier curves. We also show that the idea of selecting only extreme patient samples for training is effec-tive for improving the prediction accuracy when different gene selection methods are used. Contact