Using decision tree learning to predict the responsiveness of hepatitis C patients to drug treatment

The recommended treatment for patients with chronic hepatitis C, pegylated interferon α (PEG-IFN-α) plus rebavirin (RBV), does not provide a sustained virologic response in all patients, especially those with hepatitis C virus (HCV) genotype 1. It is therefore important to predict whether or not a new patient with HCV genotype 1 will be cured by the recommended treatment. We propose a prediction method for a new patient using a decision tree learning model based on SNPs evaluated in a genome-wide association study. By the decision tree learning for 142 Japanese patients with HCV genotype 1 (78 with null virologic response and 64 with virologic response), we can predict with high probability (93%) whether or not a new patient with HCV will be helped by the recommended treatment.

▸ We modeled drug responses using decision tree learning based on SNPs in a genome-wide association study. ▸ We can predict the drug responses of a new patient with HCV genotype 1. ▸ Responsiveness to pegylated interferon α (PEG-IFN-α) plus rebavirin (RBV) treatment was predicted. ▸ We can predict with 93% probability whether a new patient with HCV genotype 1 will be helped by drug treatment.